Year: 2019
Brain Dynamic Lab – open positions
The Human Brain Dynamic Lab
Dr. Elana Zion Golumbic
דרושים סטודנטים מצטיינים בעלי אורייטנציה חישובית חזקה
לפרוייקט חדש המשלב מדעי המוח-הנדסה
של פיתוח ממשק מוח-מכונה לשיפור יכולות מיקוד קשב
תיאור הפרוייקט: הפרוייקט הינו שיתוף פעולה בין חוקרים ממדעי המוח (דר׳ אילנה ציון גולומביק) והנדסה (פרופ׳ שרון גנות ופרופ׳ יעקב גולדברגר), שמטרתו פיתוח עזר שמיעה חכם, המבוסס ממשק מוח-מכונה, בעל יכולות מיקוד קשב לסיוע בהקשבה בסביבות רועשות. הפרוייקט יעשה שימוש ברישום גלי מוח (EEG) ותנועות עיניים, אלגוריתמים מתקדמים להפרדת אותות ולמידת עומק.
דרישות: סטודנטים מצטיינים לתואר שני או דוקטורט במדעי המוח, הנדסה או מדעי המחשב.
פתוח גם לסטודנטים מצטיינים בשנה האחרונה של התואר הראשון במדעי המוח/הנדסה הרואים את עתידם במחקר.
למעוניינים, נא לשלוח קורות חיים ופסקה קצרה על הנסיון המחקרי שלכם ל:
דר׳ אילנה ציון גולומביק elana.zion-golumbic@biu.ac.il
פרופ׳ שרון גנות: Sharon.Gannot@biu.ac.il
פרופ׳ יעקב גולדברגר: Jacob.Goldberger@biu.ac.il
משרת סטודנט
לקליניקה של המחלקה לפסיכולוגיה דרוש/ה
במעבדה לחקר פסיכותרפיה מתקיים פרויקט ייחודי בחזית המחקר עובד/ת לסיוע בניהול ותפעול מערכתחכמה לאיסוף נתונים.
במסגרת פרויקט זה נערך מעקב מחקרי שוטף אחר טיפולים המתבצעים בקליניקה לשירות הקהילה במטרה לקדם את האפקטיביות של הטיפולים. המחקר מנוהל על ידי מערכת שתוכנתה במיוחד לצרכי הקליניקה והמחקר בה שנמצאת כיום בשלבי פיתוח מתקדמים.
דרוש/ה סטודנט בעל יכולות טכניות ותכנותיות גבוהות ולמידה עצמאית.
התפקיד כולל עבודה צמודה עם מפתחי התכנה לשם היכרות עם המערכת ובמטרה לסייע בפיתוח ובפתרון תקלות.
דרישות התפקיד:
חובה – OFFICE
חובה – היכרות עם מערכות מבוססות Linux
חובה – הבנה טכנית טובה של חומרת מחשב ותכנה
רשות – הכרה של תוכנות סטטיסטיקה
רשות – יכולות בתכנות (מטלב, C)
רשות – הבנה באיסוף ופיענוח אותות פיזיולוגיים
נדרשת זמינות גבוהה
תנאים טובים למתאימים
להגשת מועמדות ולפרטים נוספים יש לשלוח קורות חיים למייל: biuresearchclinic@gmail.com
משרת סטודנט לעבודה לפי שעות
המשרה אוישה!
לפרוייקט המחקר “על מה מדינות מדברות?” (במימון ISF – הקרן הלאומית למדע) שעוסק בניתוח טקסטואלי ממוחשב דרוש/ה סטודנט/ית לעבודה לפי שעות (היקף של רבע-שליש משרה לאורך השנה האקדמית). עיקר העבודה הינה ללוות בהיבט תכנוני את הפרויקט על חלקיו שונים ולסייע בכתיבת הקוד לניתוח הטקסטים בהתבסס על גישות שונות לניתוח טקסטואלי ממוחשב.
דרש ידע בתכנות R או פייטון ורקע בניתוח ועיבוד טקסטואלי/גישות NLP.
תחילת עבודה – 1.11.2019 . להגשת מועמדות ופרטים נוספים, אנא פנו לד”ר מור מיטרני mor.mitrani@biu.ac.il
Data Science Courses, Bar-Ilan University
This table includes courses given at Bar-Ilan that may be relevant to students interested in Data Science.
Note: full and accurate course details should be taken from Bar-Ilan official course catalog. We’ll do our best to keep this table updated.
(legend – see below table)
Course # | Course Name | Department | Category | Target Audience | Points | Type | Semester | Degree | Given in TASHAPB |
---|---|---|---|---|---|---|---|---|---|
13619 | Digital Humanities and the Analysis of Hebrew Texts | Jewish Literature | NLP/Text | Exposure | 2 | Lecture | Annual | Both | y |
272000 | Introduction to Programming using python | Brain Science | Programming | Core | 1 | Lecture | B | Undergrad | y |
27208 | Introduction to Probability | Brain Science | Statistics/Probablity | Core | 1 | Lecture | A | Grad | y |
27213 | Introduction to Statistics | Brain Science | Statistics/Probablity | Core | 1.5 | Lecture | B | Undergrad | y |
27305 | Signal Processing | Brain Science | Signal Processing | Core | 1 | Lecture | B | Undergrad | y |
27436 | Neuronal Network | Brain Science | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
27437 | Information Theory and Learning Methods | Brain Science | ML/DL/NN | Core | 1.5 | Lecture | B | Undergrad | y |
275020 | Data Science Applications in Neuroscience | Brain Science | ML/DL/NN | Applied | 1 | Workshop | A | Grad | y |
275021 | Data Science Applications in Neuroscience | Brain Science | ML/DL/NN | Applied | 1 | Workshop | A | Grad | y |
275027 | Introduction to Programming with Python | Brain Science | Programming | Applied | 1 | Lecture | A | Grad | y |
27504 | Theories on Nerve Networks and Machine Learning | Brain Science | ML/DL/NN | Core | 2 | Lecture | A | Undergrad | |
27505 | Signal and Data Analysis in Neuroscience | Brain Science | Signal Processing | Core | 2 | Lecture | B | Grad | y |
278237 | Artificial intelligence: from humanoids to swarms of thinking machines | STS | AI | Exposure | 2 | Lecture | Annual | Undergrad | y |
35599 | Data Science practicum | Information Science | Data Mining / Visualization | Applied | 1 | Lecture | A | Both | y |
35603 | Algorithms I | Information Science | Algorithms/Data Structures | Applied | 2 | Lecture | A | Undergrad | y |
35605 | Big Data | Information Science | DB/Big Data | Applied | 1 | Lecture | A | Both | y |
35615 | Programming Basics | Information Science | Programming | Exposure | 1 | Lecture | B | Undergrad | y |
35616 | Advanced programming | Information Science | Programming | Applied | 1 | Lecture | B | Undergrad | y |
35617 | Advanced data analysis | Information Science | Data Mining / Visualization | Applied | 1 | Lecture | A | Both | y |
35625 | Big Data applications | Information Science | DB/Big Data | Applied | 1 | Lecture | B | Both | y |
35626 | Introduction to Data Science | Information Science | ML/DL/NN | Applied | 1.5 | Lecture | A | Both | |
35633 | Data Science introduction | Information Science | ML/DL/NN | Applied | 1 | Lecture | B | Both | y |
35712 | Digital Humanities | Information Science | Digital Humanities | Applied | 1 | Lecture | A | Both | y |
35728 | Introduction to Databases | Information Science | DB/Big Data | Applied | 1 | Lecture | A | Both | y |
35733 | Introduction to Databases | Information Science | DB/Big Data | Applied | 1 | Lecture | B | Both | y |
35809 | Geographic Information Systems | Information Science | GIS | Applied | 1 | Lecture | B | Grad | y |
35810 | Introduction to Databases | Information Science | DB/Big Data | Applied | 1 | Lecture | B | Grad | |
35819 | Medical Informatics | Information Science | Medical | Applied | 1 | Lecture | B | Grad | y |
35858 | Data Visualization | Information Science | Data Mining / Visualization | Applied | 1 | Lecture | B | Grad | y |
35867 | Introduction to Programming - Python | Information Science | Programming | Applied | 1 | Lecture | B | Grad | y |
35869 | Advanced Python | Information Science | Programming | Applied | 1 | Lecture | A | Grad | y |
35879 | The Semantic Web | Information Science | Digital Humanities | Applied | 1 | Lecture | A | Grad | y |
35880 | Algorithms 2 | Information Science | Algorithms/Data Structures | Applied | 1 | Lecture | A | Grad | |
35887 | Machine Learning | Information Science | ML/DL/NN | Applied | 1 | Lecture | B | Grad | y |
35890 | Introduction to digitization of textual and graphic information | Information Science | Digital Humanities | Applied | 1 | Lecture | B | Grad | y |
35954 | Selected issues in Digital humanities | Information Science | Digital Humanities | Exposure | 1 | Seminar | B | Grad | y |
35955 | Semantic web applications for digital humanities | Information Science | Digital Humanities | Applied | 1 | Seminar | B | Grad | y |
55002 | Intro to Statistics I | Management | Statistics/Probablity | Applied | 1 | Lecture | A | Undergrad | y |
55003 | Intro to Statistics II | Management | Statistics/Probablity | Applied | 1 | Lecture | B | Undergrad | y |
55006 | Introduction to Probability | Management | Statistics/Probablity | Applied | 1 | Lecture | B | Both | y |
55089 | Service Management | Management | DB/Big Data | Applied | 1 | Lecture | B | Both | y |
55505 | Logistic Information System Management | Management | DB/Big Data | Applied | 1 | Lecture | A | Both | y |
55703 | Information System Management in Industry | Management | DB/Big Data | Applied | 1 | Lecture | B | Both | y |
60066 | Intro to R | Psychology | Programming | Applied | 1 | Lecture | A | Grad | y |
60067 | Multilevel Modeling and Dyadic Analysis | Psychology | Data Mining / Visualization | Applied | 1 | Lecture | B | Grad | y |
66153 | Introduction to Statistics I | Economics | Statistics/Probablity | Applied | 1 | Lecture | A | Undergrad | y |
66154 | Introduction to Statistics II | Economics | Statistics/Probablity | Applied | 1 | Lecture | B | Both | y |
66862 | Python for Economists Introductory | Economics | Programming | Applied | 0.5 | Lecture | A | Undergrad | y |
66863 | Economics | Programming | Applied | 0.5 | Lecture | B | Undergrad | y | |
66880 | Econometrics of Time Series | Economics | Finance/Econometrics | Applied | 1 | Lecture | B | Undergrad | |
70647 | Text Mining | Buisness Administration | NLP/Text | Applied | 1 | Lecture | B | Physician Programming Certificate Studies | y |
70648 | Big data applications in Marketing | Buisness Administration | BI/User Behavior | Exposure | 1 | Lecture | A | Both | y |
70651 | big data management technics | Buisness Administration | DB/Big Data | Applied | 1 | Lecture | B | Both | y |
70673 | Visualization | Buisness Administration | Data Mining / Visualization | Applied | 1 | Lecture | A | Both | y |
70677 | Global Information Systems (GIS) | Buisness Administration | GIS | Applied | 1 | Lecture | B | Both | |
70680 | Data Mining with R | Buisness Administration | Data Mining / Visualization | Applied | 1 | Lecture | A | Both | y |
70784 | Data Warehousing | Buisness Administration | DB/Big Data | Applied | 1 | Lecture | B | Both | |
70798 | Storage Systems | Buisness Administration | DB/Big Data | Applied | 1 | Lecture | B | Both | y |
70833 | Introduced to Artificial Intelligence | Buisness Administration | AI | Applied | 1 | Lecture | B | Grad | y |
70949 | Data Mining and Information Disclosure | Buisness Administration | Data Mining / Visualization | Applied | 1 | Lecture | B | Grad | y |
75145 | Computer Applications in Documentation and Study of Place | Geography | GIS | Applied | 1 | Lecture | B | Undergrad | |
75335 | Advanced GIS A | Geography | GIS | Applied | 2 | Lecture | A | Undergrad | |
75373 | Introduction to GIS | Geography | GIS | Applied | 1.5 | Lecture | A | Undergrad | y |
75967 | Python Scripting for GIS | Geography | GIS | Applied | 1.5 | Lecture | B | Grad | y |
80235 | *Introduction to Programming using python | Life | Programming | Applied | 1 | Lecture | A | Undergrad | |
80303 | Advanced Methods in Medical Image Processing | Life | Medical | Applied | 1 | Lecture | B | Both | y |
80376 | Matlab for Biologists | Life | Programming | Applied | 1 | Lecture | A | Both | y |
80392 | Computational Genomics | Life | Bio | Applied | 1 | Lecture | A | Both | y |
80397 | Statistics and Data Science | Life | Data Mining / Visualization | Applied | 0.5 | Lecture | A | Both | |
80512 | Computational Biology | Life | Bio | Applied | 1 | Lecture | B | Both | y |
80513 | Bioinformatics | Life | Bio | Applied | 1 | Lecture | B | Both | y |
80515 | Introduction to Computing | Life | Programming | Applied | 1.5 | Lecture | A | Undergrad | y |
80534 | Biostatistics and Introduction to Clinical Trails | Life | Bio | Applied | 1 | Lecture | B | Both | y |
80586 | Machine learning and applications for biological data analysis | Life | Bio | Applied | 1 | Lecture | B | Both | y |
80665 | Medical Informatics | Life | Medical | Applied | 1 | Lecture | B | Both | y |
80672 | Advanced Tools to Genome Analysis | Life | Bio | Applied | 1 | Lecture | A | Both | |
80675 | Clinical Informatics - Clinical Data Mining | Life | Bio | Applied | 0.5 | Lecture | A | Both | |
80724 | Python Programming for Scientific Research | Life | Bio | Applied | 1 | Lecture | B | Both | |
80725 | Deep Learning and Artificial Intelligence in Medicine | Life | Medical | Applied | 1 | Lecture | B | Both | |
81936 | Digital Image Processing | Medicine | Image | Applied | 1 | Lecture | B | Grad | y |
81958 | Text Mining for Cancer Research | Medicine | Medical | Applied | 1 | Lecture | A | Grad | y |
83003 | MATLAB programming and applications | Engineering | Programming | Applied | Lab | B | Undergrad | y | |
83011 | Workshop in Python Programming | Engineering | Programming | Core | 1 | Lecture | B | Both | y |
83214 | Tools for Numerical Analysis | Engineering | Math | Core | 1 | Lecture | B | Undergrad | y |
83216 | Introduction to Statistics and Probability | Engineering | Statistics/Probablity | Core | 1.5 | Lecture | A | Undergrad | y |
83223 | Object Oriented Programming | Engineering | Programming | Core | 1 | Lecture | A | Undergrad | y |
83224 | Data Structures and Algorithms II | Engineering | Algorithms/Data Structures | Core | 1.5 | Lecture | B | Undergrad | y |
83245 | Signals and Systems | Engineering | Signal Processing | Core | 1.5 | Lecture | B | Undergrad | y |
83302 | Random Signals and Noise | Engineering | Signal Processing | Core | 1.5 | Lecture | A | Undergrad | y |
83320 | Digital Signal Processing I | Engineering | Signal Processing | Core | 1.5 | Lecture | B | Undergrad | y |
83321 | Statistical Algorithms for Signal Processing | Engineering | Signal Processing | Core | 1.5 | Lecture | B | Undergrad | y |
83412 | Genetics and Molecular Biology | Engineering | Bio | Applied | 1 | Lecture | B | Undergrad | y |
83414 | Biological data science | Engineering | Bio | Applied | 1.5 | Lecture | B | Undergrad | y |
83420 | Statistical Analysis of Data | Engineering | Statistics/Probablity | Core | 1.5 | Lecture | B | Undergrad | y |
83456 | Design and Analysis of Algorithms | Engineering | Algorithms/Data Structures | Core | 1 | Lecture | A | Undergrad | y |
83459 | Software Engineering | Engineering | Programming | Core | 1 | Lecture | B | Undergrad | y |
83620 | Information Theory | Engineering | Math | Core | 1 | Lecture | A | Both | y |
83622 | Introduction to Machine Learning | Engineering | ML/DL/NN | Core | 1 | Lecture | B | Both | y |
83623 | Models and Mathematical Analysis of Networks | Engineering | Signal Processing | Core | 1 | Lecture | A | Both | y |
83624 | Digital Signal Processing II | Engineering | Signal Processing | Core | 1.5 | Lecture | A | Both | y |
83629 | Digital Image Processing | Engineering | Image | Core | 1 | Lecture | B | Both | y |
83633 | Digital Geometric Processing II | Engineering | Bio | Applied | 1 | Lecture | A | Both | y |
83635 | Reinforcement-based learning | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
83641 | Shape Optimization & Understanding | Engineering | Geometry | Core | 1 | Lecture | B | Both | |
83643 | Machine learning theory | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
83656 | Digital Processing of Geometry | Engineering | Geometry | Core | 1 | Lecture | B | Both | y |
83665 | Computational Biology | Engineering | Bio | Applied | 1 | Lecture | A | Both | y |
83666 | Control ofTheory for Biological Systems | Engineering | Bio | Applied | 1 | Lecture | A | Both | y |
83674 | Quantum machine learning | Engineering | Quantom | Core | 1 | Lecture | B | Both | y |
83676 | Data Mining | Engineering | Data Mining / Visualization | Applied | 1 | Lecture | B | Both | y |
83692 | Social networks | Engineering | Networks | Core | 1 | Lecture | B | Undergrad | y |
83805 | Continuous and Combinatorial Optimization | Engineering | Optimization | Core | 1 | Lecture | A | Teacher Certification | y |
83806 | Random Processes | Engineering | Statistics/Probablity | Core | 1.5 | Lecture | B | Grad | y |
83807 | Quantum Computing | Engineering | Quantom | Core | 1.5 | Lecture | A | Grad | y |
83819 | Unsupervised Learning | Engineering | ML/DL/NN | Core | 1 | Lecture | B | Both | y |
83841 | Statistical Machine Learning | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
83843 | deep generative models | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
83867 | Probabilistic Methods and Algorithms | Engineering | Algorithms/Data Structures | Core | 1 | Lecture | B | Grad | |
83876 | Decision Support Systems in medical imaging | Engineering | Medical | Applied | 1 | Lecture | B | Grad | y |
83880 | Seminar/Advanced Topics in Signal Processing | Engineering | Signal Processing | Core | 1 | Lecture | B | Grad | y |
83881 | Digital speech processing | Engineering | Speech | Core | 1 | Lecture | B | Grad | y |
83882 | Deep Learning | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
83887 | Spatial Signal Processing | Engineering | Signal Processing | Core | 1 | Lecture | B | Grad | |
83888 | Computer Vision | Engineering | Image | Core | 1 | Lecture | B | Grad | y |
83889 | Advanced Topics in Statistical Signal Processing | Engineering | Signal Processing | Core | 1 | Lecture | A | Grad | y |
83900 | Discovery Theory | Engineering | Math | Core | 1 | Lecture | A | Grad | y |
83901 | Introduction to Data Science with Python | Engineering | ML/DL/NN | Applied | 1 | Lecture | B | Grad | |
83905 | Seminar/Advanced Topics in Machine Learning and Data Processing | Engineering | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
83906 | Independence-based Blind Source Separation | Engineering | Statistics/Probablity | Core | #N/A | #N/A | Grad | y | |
83907 | Advanced topics in deep learning | Engineering | ML/DL/NN | Core | 1 | Lecture | B | Grad | y |
83908 | Advanced topics in differential privacy | Engineering | Privacy | Core | 1 | Lecture | B | Grad | y |
83920 | Parallel Computation using a GPU | Engineering | Algorithms/Data Structures | Core | 1 | Lecture | A | Grad | |
83979 | Statistics and Data Analysis | Engineering | Data Mining / Visualization | Applied | 1 | Lecture | B | Grad | y |
83983 | Process Modeling and Mining | Engineering | Data Mining / Visualization | Core | 1 | Lecture | B | Both | y |
84107 | Statistics and Probability for Chemists (SPC) | Chemistry | Statistics/Probablity | Applied | 1 | Lecture | A | Undergrad | y |
84190 | Introduction to Computers in Chemistry | Chemistry | Programming | Applied | 1 | Lecture | B | Undergrad | |
84291 | Introduction to programming in Python for chemistry | Chemistry | Programming | Applied | 1 | Lecture | B | Both | y |
84328 | Computational Chemistry | Chemistry | Bio | Applied | 1 | Lecture | B | Undergrad | y |
84846 | Introduction to Cheminformatics | Chemistry | Bio | Applied | 1 | Lecture | A | Grad | y |
86156 | Probability and Statistics for Physicists | Physics | Statistics/Probablity | Applied | 1.5 | Lecture | B | Undergrad | y |
86164 | Introduction to Computers in Physics | Physics | Programming | Applied | 1 | Lecture | A | Undergrad | y |
86605 | Data science for physicists | Physics | ML/DL/NN | Applied | 1.5 | Lecture | A | Both | y |
88151 | Computer Applications in Math | Math | Programming | Core | 1 | Lecture | B | Undergrad | |
88153 | Introduction to Mathematical Programming | Math | Programming | Core | 1 | Lecture | B | Undergrad | |
88165 | Introduction to Probabilitiy and Statistics | Math | Statistics/Probablity | Core | 2 | Lecture | B | Undergrad | y |
88170 | Introduction to Computing | Math | Programming | Core | 1.5 | Lecture | A | Undergrad | y |
88174 | Introduction to Object Oriented Programming | Math | Programming | Core | 1 | Lecture | B | Undergrad | y |
88263 | Introduction to Statistics | Math | Statistics/Probablity | Core | 2 | Lecture | A | Grad | y |
88280 | Data Structures and Algorithms | Math | Algorithms/Data Structures | Core | 2 | Lecture | A | Grad | y |
88584 | Image Processing | Math | Image | Core | 1 | Lecture | A | Both | y |
88615 | Introduction to Probabilitiy and Statistics | Math | Statistics/Probablity | Core | 1 | Lecture | B | Both | |
886210 | Risk management and time series | Math | Finance/Econometrics | Core | 1 | Lecture | Summer | Both | |
88622 | Introduction to Probabilitiy and Statistics | Math | Statistics/Probablity | Core | 1.5 | Lecture | A | Grad | y |
88623 | Probability and Stochastic Processes | Math | Statistics/Probablity | Core | 1.5 | Lecture | B | Grad | y |
88624 | Analysis of Statistical Data | Math | Data Mining / Visualization | Core | 1 | Lecture | Summer | Both | y |
88631 | Introduction to Probabilitiy and Statistics | Math | Statistics/Probablity | Core | 0.5 | Lecture | A | Both | y |
886788 | Data Science Seminar | Math | ML/DL/NN | Core | 1 | Seminar | B | Grad | y |
886960 | Introduction to Programming using python | Math | Programming | Applied | 1 | Lecture | B | Undergrad | y |
886961 | Python Programming Workshop | Math | Programming | Applied | 1 | Lecture | A | Both | y |
886970 | Data Processing, Analysis and Visualization | Math | Data Mining / Visualization | Applied | 1 | Lecture | B | Undergrad | y |
886971 | Applied Machine Learning | Math | ML/DL/NN | Applied | 1 | Lecture | A | Both | y |
886972 | Big Data | Math | DB/Big Data | Applied | 1 | Lecture | A | Both | y |
886980 | Networks and Complexity in the Real World | Math | Networks | Applied | 1 | Lecture | B | Grad | y |
88760 | Introduction to Statistics | Math | Statistics/Probablity | Core | 1 | Lecture | A | Grad | y |
88761 | Introduction to Statistics II | Math | Statistics/Probablity | Core | 1 | Lecture | B | Grad | y |
88775 | Statistical Theory | Math | Statistical Theory | Core | 1.5 | Lecture | A | Grad | y |
88778 | Network Science | Math | Networks | Core | 1.5 | Lecture | B | Grad | |
88779 | Random graphs and networks | Math | Networks | Core | Lecture | B | Grad | ||
88780 | Supervised and Unsupervised Learning | Math | ML/DL/NN | Core | 1 | Lecture | A | Grad | y |
887810 | Introduction to artificial intelligence | Math | AI | Core | 1 | Lecture | B | Both | y |
88784 | Optimization | Math | Optimization | Core | 1.5 | Lecture | A | Both | y |
88962 | Probability and Stochastic Processes | Math | Statistics/Probablity | Core | 1.5 | Lecture | A | Undergrad | y |
89110 | Introduction to Computer Science | Computer Science | Programming | Core | 1.5 | Lecture | A | Undergrad | y |
89111 | Introduction to Object Oriented Programming | Computer Science | Programming | Core | 1 | Lecture | B | Grad | y |
89120 | Data Structures | Computer Science | Algorithms/Data Structures | Core | 1 | Lecture | B | Both | y |
89210 | Algorithmic Programming I | Computer Science | Algorithms/Data Structures | Core | 1 | Lecture | A | Both | y |
89211 | Algorithmic Programming II | Computer Science | Algorithms/Data Structures | Core | 1 | Lecture | B | Grad | y |
89220 | Algorithms 1 | Computer Science | Algorithms/Data Structures | Core | 1.5 | Lecture | A | Both | y |
89255 | Graph Theory | Computer Science | Networks | Core | 1.5 | Lecture | A | Both | y |
89262 | General Probability | Computer Science | Statistics/Probablity | Core | 1 | Lecture | A | Both | y |
89264 | Biostatistics | Computer Science | Bio | Core | 1.5 | Lecture | A | Both | y |
89276 | Numeric Methods | Computer Science | Math | Core | 1 | Lecture | B | Undergrad | |
89312 | Programming in a multi-processor environment | Computer Science | Programming | Core | 1 | Lecture | B | Both | y |
89322 | Algorithms 2 | Computer Science | Algorithms/Data Structures | Core | 1 | Lecture | B | Both | y |
89350 | Introduction to Communication Networks | Computer Science | Networks | Core | 1 | Lecture | A | Both | y |
89362 | General Statistics | Computer Science | Statistics/Probablity | Core | 1 | Lecture | B | Both | y |
894043 | Advanced Seminar in natural lagquage processing | Computer Science | NLP/Text | Core | 1 | Seminar | B | Both | y |
894044 | Research seminar in natural language processing â part 2 | Computer Science | NLP/Text | Core | 1 | Seminar | B | Undergrad | y |
89408 | Advanced Seminar in Algorithmic Game Theory | Computer Science | Algorithms/Data Structures | Core | 1 | Seminar | B | Undergrad | y |
894112 | User Behavior Machine Learning Algorithms Seminar | Computer Science | ML/DL/NN | Core | 1 | Seminar | B | Undergrad | y |
89421 | Seminar/Strategic Planning for Robots | Computer Science | Robotics | Core | 1 | Seminar | B | Undergrad | |
894483 | Seminar in Machine Learning and Speech Processing | Computer Science | Speech | Core | 1 | Seminar | B | Undergrad | |
89452 | Seminar/Web, Crowd and Big Data Management | Computer Science | DB/Big Data | Core | 1 | Seminar | A | Undergrad | y |
894531 | Seminar: Learning Algorithms and Natural Language Processing | Computer Science | NLP/Text | Core | 1 | Seminar | B | Undergrad | y |
89454 | Structures for Semantics | Computer Science | NLP/Text | Core | 1 | Seminar | A | Undergrad | |
89460 | Seminar: From Text to Information | Computer Science | NLP/Text | Core | 1 | Seminar | B | Undergrad | |
89471 | Seminar in Pattern Recognition | Computer Science | Image | Core | 1 | Seminar | A | Undergrad | |
894851 | Advanced Seminar in Text Understanding | Computer Science | NLP/Text | Core | 1 | Seminar | B | Undergrad | |
89493 | Seminar/Applied Machine Learning for NLP | Computer Science | NLP/Text | Core | 1 | Seminar | B | Undergrad | y |
89511 | Machine Learning | Computer Science | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
89512 | Computational Biology | Computer Science | Bio | Applied | 1 | Lecture | B | Both | y |
89519 | Machine Learning for Healthcare | Computer Science | Medical | Applied | 1 | Lecture | B | Both | y |
89542 | Management of Big Web Data | Computer Science | DB/Big Data | Core | 1 | Lecture | A | Both | y |
89560 | Image Processing | Computer Science | Image | Core | 1 | Lecture | A | Both | y |
89570 | Artificial Intelligence | Computer Science | AI | Core | 1 | Lecture | A | Both | y |
89581 | Database Systems | Computer Science | DB/Big Data | Core | 1 | Lecture | B | Both | y |
89594 | Formal Representations for Natural Languages | Computer Science | NLP/Text | Core | 1 | Lecture | B | Both | y |
89608 | Speech Recognition | Computer Science | Speech | Core | 1 | Lecture | B | Both | |
89641 | Topics in Information Theory | Computer Science | Math | Core | 1 | Lecture | B | Both | y |
89654 | Advanced Methods in Machine Learning | Computer Science | ML/DL/NN | Core | 1 | Lecture | B | Both | |
896541 | Practical topics in Machine Learning | Computer Science | ML/DL/NN | Applied | 1 | Lecture | B | Both | |
89679 | Workshop in Databases | Computer Science | DB/Big Data | Core | 1 | Workshop | A | Both | y |
89680 | Natural Language Processing | Computer Science | NLP/Text | Core | 1.5 | Lecture | B | Both | y |
89685 | Introduction to Robotics | Computer Science | Robotics | Core | 1 | Lecture | A | Both | y |
89687 | Deep Learning Methods for Texts and Sequences | Computer Science | NLP/Text | Core | 1 | Lecture | B | Both | y |
896871 | Deep learning | Computer Science | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
896872 | Deep Neural Network for Computer Vision | Computer Science | ML/DL/NN | Core | 1 | Lecture | B | Both | |
896873 | Reinforcement Learning | Computer Science | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
896874 | Deep learning for perception | Computer Science | Programming | Core | 1 | Lecture | A | Grad | |
89688 | Statistical Machine Translation | Computer Science | NLP/Text | Core | 1 | Lecture | B | Both | y |
89919 | Applied Probabilistic Models in Computer Science | Computer Science | ML/DL/NN | Core | 1 | Lecture | A | Both | y |
89950 | Advanced Topics in Artificial Intelligence | Computer Science | ML/DL/NN | Core | 1 | Lecture | B | Both | y |
89991 | Machine Learning Colloqium | Computer Science | ML/DL/NN | Core | Colloquium | Annual | Both | y | |
996728 | Law and Big Data | Law | Data Mining / Visualization | Applied | 1 | Lecture | B | Both | y |
999025 | Data Science for Jurists | Law | Data Mining / Visualization | Exposure | 1.5 | Lecture | A | Both | y |
LEGEND
Target Audience:
- Core: Students interested in understanding in depth how core data science methods and algorithms work, and in developing such methods.
Typical student profile: rigorous programming, mathematical and algorithmic skills. - Applied: Students interested in learning applied data science methods for the purpose of applying them extensively in various domains.
Typical student profile: basic programming and analytical skills, which may be acquired through relevant courses listed here for this audience. - Exposure: Students interested in high-level understanding of data science methods and their potential in various fields, in applying them using tools which do not require programming, and in social and ethical aspects of data science.
Typical student profile: students from broad disciplines who may not have programming or mathematical skills, or – for some courses – students interested in social, legal or ethical aspects of data science.
Points:
- Usually, each point corresponds to two hours in one semester.
Category:
- Fundamentals:
- Statistics/Probablity
- Programming
- Math
- Algorithms/Data Structures
- CORE
- Signal Processing
- ML/DL/NN – Machine Learning, Deep Learning, Neural Networks
- Optimization
- DB/Big Data
- Networks
- Data Mining / Visualization
- Statistical Theory
- AI – Artificial Intelligence (Agents, Problem Solving, Planning, Search algorithms, …)
- Privacy
- Game Theory
- APPLICATIONS
- NLP/Text
- Image – Image, Video Processing, Pattern Recognition
- Speech – Speech and Audio Processing
- Bio – BioInformatics, Computational Biology
- Finance/Econometrics –
- GIS – Geographic Information Systems (Geography)
- BI/User Behavior – Business Intelligence
- Digital Humanities
- Medical
- Robotics
Disclaimer
מרצים/ות לקורסים במדעי הנתונים
דרושים מרצים/ות לקורסים במדעי הנתונים
אוניברסיטת בר-אילן מציעה מספר רב של קורסים בתחום מדע הנתונים הניתנים במחלקות השונות באוניברסיטה. אנו מחפשים לשנת הלימודים הבאה (תש”פ) מרצים/ות לקורסים הבאים:
- מבוא לתכנות בשפת פייתון (קורס תכנות בסיסי בשפת פייתון לתלמידי מדעי הטבע והחיים)
- מבוא לניתוח, עיבוד וויזואליזציה של נתונים לתלמידי מדעי הטבע והחיים (קורס חדש)
- עיבוד תמונה (קורס image processing בפקולטה להנדסה עם דגש על שיטות למידה)
- רשתות נוירונים (קורס בסיסי ברשתות נוירונים בתכנית תואר ראשון במדעי המוח)
- NLP for legal-tech (קורס חדש במסגרת תכנית לתואר שני בlega-ltech בפקולטה למשפטים)
כישורים נדרשים (לכל המשרות):
- ניסיון בהוראה
- רקע במדעי הנתונים ובתחום הספציפי של הקורס
- בעלי תואר שלישי בתחום רלוונטי (או תואר שני עם רקע וניסיון מתאים)
לפרטים נוספים נא לפנות ל-dsi@biu.ac.il
Instructor for “NLP for legal-tech” course
Bar-Ilan law faculty is looking for an instructor to teach a new course on NLP for legal-tech in an MA program in legal studies which is directed to computer science and engineering graduates:
Core requirements
- Ph.D. in computer science or related field ·
- Strong NLP background
- Ability to co-op with lectures from Bar-Ilan law faculty that will provide background on legal tech and how it is embedded in legal practice and user needs
- Ability to co-op (and to initiate contact) with people from the legal tech industry.
- Law people – customer perspective/needs, people who experienced legal tech
- Legal-tech people – use cases from the industry
Course content
This is a new NLP course that will provide students the basic theoretical foundation of NLP techniques along with practical NLP usage in the legaltech context. Emphasis will be on hands-on exercises and examples drawing on legal texts (eg, statutes, case law).
Hebrew is highly desired – at least for some of the exercises.
Possible topics/examples to be included:
- Classify/cluster similar judges
- Extract citations
- Classify patents to domains
- Terms and conditions
- Application of deep learning solutions
- How AI + NLP models can deal with legal dilemmas
Course should be planned for 13 weeks * 2 hours;
Will be given in Spring 2020.
Examples
Contact:
Prof Oren Perez: oren.perez@biu.ac.il, Dean of the Faculty of Law at Bar-Ilan University
Dr. Oren Glickman: oren.glickman@biu.ac.il. The Data Science Institute at Bar Ilan University.
Deep Learning expert to support data science research at Bar-Ilan University
Job Description
Bar-Ilan University, is a world leader in the field of data science and artificial intelligence. Bar-Ilan is setting up a computing environment, designed specifically for deep-learning research which is based on NVIDIA’s flagship DGX-1 computers and will be the largest of its kind in academia in Israel. The institutional computing environment will support a large number of faculty members and grad students from across Bar-Ilan.
To take proper advantage of the unique GPU-based supercomputer, we seek prospective candidates for a new position expected within several months, concerned with supporting the research that depends on the new hardware. The candidate we are seeking has to have a PhD degree in a relevant field, with good knowledge of the latest deep learning working environments.
The main responsibilities of the candidate include:
- Optimize the integrated hardware system for various use-cases, taking into account dependencies and interactions between algorithms, software, drivers and hardware.
- Advise researchers about state-of-the-art platforms, tools and development processes in deep learning, including advising individual research projects regarding deep-learning code development and optimization.
- Support the large amount of researchers who will be using the computational environment. Involving, as needed, the support of the Bar-Ilan Computing Center or the 3rd party provider chosen to setup the environment (TeraSky).
- Training researchers on how to properly use the computational environment including use of queuing systems, containers, GPU enabled code etc. In addition to training, job will involve teaming with researches with hands-on sessions to help write and adopt their code to a GPU based shared environment, including leveraging the DGX properties.
- Support of porting and optimization of existing HPC code to a GPU setting.
- Ongoing monitoring of the system and its utilization. Identifying bottlenecks in the system and groups or individuals who are not utilizing the hardware properly. Configuration and enhancement of the working environment and its interfaces as needed to support usage needs.
In this position you will be working in a leading research institution on an advanced computational environment and will be able to make a significant contribution to the most recent research in the field.
Required Qualifications
- A PhD in a relevant field
- Knowledge of deep learning algorithms and frameworks
- Familiarity with queuing systems, containers and GPU enabled code
- Expert level python coding skills
- Excellent communication and support skills
- Passion to work in an academic research setting
Contact dsi@biu.ac.il for details.
Application deadline: Feb 28, 2019.
Governance through Global Networks and Corporate Signalling
(Recent post by Prof. Perez, Prof. Reuven Cohen and Nir Schreiber in the University of Oxford business law blog)
Global governance is in crisis. The conventional treaty-based system is struggling to cope with the multiple challenges faced by global society. This failure is evident in various areas, including climate change, protection of labour rights across global supply and commodity chains, global bio-diversity, and more. This governance crisis has motivated the creation of multiple private corporate social responsibility (CSR) schemes that operate alongside the treaty-based system. These transnational CSR schemes include voluntary corporate codes, environmental management systems, various labelling and certification schemes, sustainability reporting standards, and global ranking schemes. Most CSR schemes include both a guiding component (code, standard), and a compliance element. Through their capacity to directly regulate the behaviour of corporations on a global scale, CSR regimes offer a way to circumvent the regulatory weaknesses of the international treaty system. Various authors, however, have voiced scepticism about the credibility of CSR regimes, arguing that they have no real regulatory power.
In a recent paper ‘Governance through Global Networks and Corporate Signalling’, published in Regulation and Governance (2018), we study the credibility of CSR schemes, based on a network analysis of an original dataset of CSR schemes and certified firms. We argue that the authority of CSR schemes should be viewed as an emergent, network-based property. The various CSR schemes constitute a multi-layered network of closely connected institutions. A multi-layered network evolves when actors are connected through more than one type of socially relevant tie.
To expose the network structure of the CSR system, we analysed it as an affiliation network which contains 49 CSR regimes and 31,987 firms (the data refer to December 31, 2014). Our analysis focused on the induced graph which depicts the relations between the CSR schemes (each node in the graph represents a CSR scheme). Two nodes are connected by an edge if a firm holds a certificate from both (or is a joint member of both). The figure below depicts the results of the analysis of a graph of the induced CSR schemes network. It demonstrates that the CSR system should indeed be viewed as a network and not as a disconnected system of transnational regulators. We show in the article that the schemes are also connected through direct organisational ties.
Figure 1: The Induced (Affiliation) CSR-Scheme Network (ANC)
Central nodes with large degrees are denoted by dark filled circles. Peripheral nodes with a small degree are light coloured. Note the single unconnected node of PT at the bottom.
We link this structural argument to a phenomenon we call ‘networked signalling’. Firms that want to use their commitment to sustainability values as a way to enhance their reputation must find a way to credibly signal their commitment. We distinguish between firms that join CSR schemes and are committed to implementing their norms (‘genuinely sustainable firms or green’) and firms that join CSR schemes but have no intention to implement them (‘greenwashers’). The challenge for genuinely sustainable firms is to find a way to distinguish themselves from ‘greenwashers’ that may produce false signals.
We argue that ‘networked signalling’ (‘NS model’) constitutes a possible solution to this communication dilemma. Firms signal their commitment to sustainability by linking, through certification or membership, to multiple CSR schemes. The inspiration for this argument comes from the model of costly signalling that was developed (independently) by the biologist Amotz Zahavi and the economist Michael Spence. The puzzle at the core of Zahavi and Spence’s work is this: why do animals and humans produce costly and potentially detrimental signals? Prominent examples from biology include the stotting behavior of gazelles, and the peacock’s tail; examples from the economic literature include the costs of an ivy league MBA degree or advertising expenditure. Zahavi and Spence argued that this seemingly puzzling behaviour is a signalling device (which Zahavi called the “handicap principle”). Both people and animals use costly signals to convey their fitness and to distinguish themselves from unfit individuals.
In the corporate world, firms use certification or membership in CSR schemes to signal their commitment to sustainability values and to distinguish themselves from ‘greenwashers’. What makes certification or membership in CSR schemes a credible signal is the differential cost structure of multiple certifications. The cost of reliable quality signals is higher for a ‘greenwashing’ firm than for a ‘green’ one. This is because the costs of maintaining a deceitful organisational structure (in which an organisation commits to a CSR scheme with no intention of implementing it), increases with the number of certifications or memberships the organisation holds. These costs reflect both the direct costs of maintaining a decoupled structure and the expected reputational costs that may accrue if the deceit is exposed. When the differential cost condition is satisfied, a separating equilibrium that distinguishes between truly ‘sustainable’ firms and ‘greenwashers’ emerges.
According to the NS model firms with multiple certifications should display a stronger CSR performance than do their peers with fewer certifications. To test this hypothesis, we compared our data on multiple certifications with data on global CSR rankings, obtained from Dow Jones Sustainability Indices (DJSI) and FTSE4Good, which are widely considered to be credible proxies for good CSR performance. We found, first, that firms selected as constituents of either the DJSI or the FTSE4Good sustainability indices are more likely to be certified by at least one CSR code, than are firms that were not selected. We found, further that a firm that is certified by multiple schemes is more likely to be included in the indices than one with fewer certifications. By showing that firms with a larger number of certifications demonstrate stronger sustainability performance these findings provide tentative support to the synergistic argument. By demonstrating a positive correlation between certification by multiple CSR schemes and sustainability performance, our analysis shows that certification or membership in CSR schemes is not just cheap talk.
Oren Perez is Dean at the Bar-Ilan Law Faculty, Israel.
Reuven Cohen is Associate Professor at the Department for Mathematics, Bar-Ilan University.
Nir Schreiber is PhD Student at the Department for Mathematics, Bar-Ilan University.