open research assistant position

המשרה אוישה!
לפרויקט סדר יום פוליטי בישראל הממומן על ידי הקרן הישראלית למדע דרוש עוזר מחקר בהיקף של 20 שעות חודשיות. העבודה דורשת הורדת נתונים משרתים ממשלתתיים scraping, סידור קבצי נתונים ממוחשב והמרת קבצים .
דרישות התפקיד: ידע בתיכנות בפייטון ו R וכן באקסל
תחילת העבודה: מיידית
לפרטים נא לפנות לדר’ אילנה שפייזמן ilana.shpaizman@biu.ac.il

Brain Dynamic Lab – open positions

 

The Human Brain Dynamic Lab

    Dr. Elana Zion Golumbic

www.golumbiclab.org

 

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)

Data Science Related Courses at Bar-Ilan University
Course #Course NameDepartmentCategoryTarget AudiencePointsTypeSemesterDegreeGiven in TASHAPB
13619Digital Humanities and the Analysis of Hebrew TextsJewish LiteratureNLP/TextExposure2LectureAnnualBothy
272000Introduction to Programming using pythonBrain ScienceProgrammingCore1LectureBUndergrady
27208Introduction to ProbabilityBrain ScienceStatistics/ProbablityCore1LectureAGrady
27213Introduction to StatisticsBrain ScienceStatistics/ProbablityCore1.5LectureBUndergrady
27305Signal ProcessingBrain ScienceSignal ProcessingCore1LectureBUndergrady
27436Neuronal NetworkBrain ScienceML/DL/NNCore1LectureAGrady
27437Information Theory and Learning MethodsBrain ScienceML/DL/NNCore1.5LectureBUndergrady
275020Data Science Applications in NeuroscienceBrain ScienceML/DL/NNApplied1WorkshopAGrady
275021Data Science Applications in NeuroscienceBrain ScienceML/DL/NNApplied1WorkshopAGrady
275027Introduction to Programming with PythonBrain ScienceProgrammingApplied1LectureAGrady
27504Theories on Nerve Networks and Machine LearningBrain ScienceML/DL/NNCore2LectureAUndergrad
27505Signal and Data Analysis in NeuroscienceBrain ScienceSignal ProcessingCore2LectureBGrady
278237Artificial intelligence: from humanoids to swarms of thinking machinesSTSAIExposure2LectureAnnualUndergrady
35599Data Science practicumInformation ScienceData Mining / VisualizationApplied1LectureABothy
35603Algorithms IInformation ScienceAlgorithms/Data StructuresApplied2LectureAUndergrady
35605Big DataInformation ScienceDB/Big DataApplied1LectureABothy
35615Programming BasicsInformation ScienceProgrammingExposure1LectureBUndergrady
35616Advanced programmingInformation ScienceProgrammingApplied1LectureBUndergrady
35617Advanced data analysisInformation ScienceData Mining / VisualizationApplied1LectureABothy
35625Big Data applicationsInformation ScienceDB/Big DataApplied1LectureBBothy
35626Introduction to Data ScienceInformation ScienceML/DL/NNApplied1.5LectureABoth
35633Data Science introductionInformation ScienceML/DL/NNApplied1LectureBBothy
35712Digital HumanitiesInformation ScienceDigital HumanitiesApplied1LectureABothy
35728Introduction to DatabasesInformation ScienceDB/Big DataApplied1LectureABothy
35733Introduction to DatabasesInformation ScienceDB/Big DataApplied1LectureBBothy
35809Geographic Information SystemsInformation ScienceGISApplied1LectureBGrady
35810Introduction to DatabasesInformation ScienceDB/Big DataApplied1LectureBGrad
35819Medical InformaticsInformation ScienceMedicalApplied1LectureBGrady
35858Data VisualizationInformation ScienceData Mining / VisualizationApplied1LectureBGrady
35867Introduction to Programming - PythonInformation ScienceProgrammingApplied1LectureBGrady
35869Advanced PythonInformation ScienceProgrammingApplied1LectureAGrady
35879The Semantic WebInformation ScienceDigital HumanitiesApplied1LectureAGrady
35880Algorithms 2Information ScienceAlgorithms/Data StructuresApplied1LectureAGrad
35887Machine LearningInformation ScienceML/DL/NNApplied1LectureBGrady
35890Introduction to digitization of textual and graphic informationInformation ScienceDigital HumanitiesApplied1LectureBGrady
35954Selected issues in Digital humanitiesInformation ScienceDigital HumanitiesExposure1SeminarBGrady
35955Semantic web applications for digital humanitiesInformation ScienceDigital HumanitiesApplied1SeminarBGrady
55002Intro to Statistics IManagementStatistics/ProbablityApplied1LectureAUndergrady
55003Intro to Statistics IIManagementStatistics/ProbablityApplied1LectureBUndergrady
55006Introduction to ProbabilityManagementStatistics/ProbablityApplied1LectureBBothy
55089Service ManagementManagementDB/Big DataApplied1LectureBBothy
55505Logistic Information System ManagementManagementDB/Big DataApplied1LectureABothy
55703Information System Management in IndustryManagementDB/Big DataApplied1LectureBBothy
60066Intro to RPsychologyProgrammingApplied1LectureAGrady
60067Multilevel Modeling and Dyadic AnalysisPsychologyData Mining / VisualizationApplied1LectureBGrady
66153Introduction to Statistics IEconomicsStatistics/ProbablityApplied1LectureAUndergrady
66154Introduction to Statistics IIEconomicsStatistics/ProbablityApplied1LectureBBothy
66862Python for Economists IntroductoryEconomicsProgrammingApplied0.5LectureAUndergrady
66863EconomicsProgrammingApplied0.5LectureBUndergrady
66880Econometrics of Time SeriesEconomicsFinance/EconometricsApplied1LectureBUndergrad
70647Text MiningBuisness AdministrationNLP/TextApplied1LectureBPhysician Programming Certificate Studiesy
70648Big data applications in MarketingBuisness AdministrationBI/User Behavior Exposure1LectureABothy
70651big data management technicsBuisness AdministrationDB/Big DataApplied1LectureBBothy
70673VisualizationBuisness AdministrationData Mining / VisualizationApplied1LectureABothy
70677Global Information Systems (GIS)Buisness AdministrationGISApplied1LectureBBoth
70680Data Mining with RBuisness AdministrationData Mining / VisualizationApplied1LectureABothy
70784Data WarehousingBuisness AdministrationDB/Big DataApplied1LectureBBoth
70798Storage SystemsBuisness AdministrationDB/Big DataApplied1LectureBBothy
70833Introduced to Artificial IntelligenceBuisness AdministrationAIApplied1LectureBGrady
70949Data Mining and Information DisclosureBuisness AdministrationData Mining / VisualizationApplied1LectureBGrady
75145Computer Applications in Documentation and Study of PlaceGeographyGISApplied1LectureBUndergrad
75335Advanced GIS AGeographyGISApplied2LectureAUndergrad
75373Introduction to GISGeographyGISApplied1.5LectureAUndergrady
75967Python Scripting for GISGeographyGISApplied1.5LectureBGrady
80235*Introduction to Programming using pythonLifeProgrammingApplied1LectureAUndergrad
80303Advanced Methods in Medical Image ProcessingLifeMedicalApplied1LectureBBothy
80376Matlab for BiologistsLifeProgrammingApplied1LectureABothy
80392Computational GenomicsLifeBioApplied1LectureABothy
80397Statistics and Data ScienceLifeData Mining / VisualizationApplied0.5LectureABoth
80512Computational BiologyLifeBioApplied1LectureBBothy
80513BioinformaticsLifeBioApplied1LectureBBothy
80515Introduction to ComputingLifeProgrammingApplied1.5LectureAUndergrady
80534Biostatistics and Introduction to Clinical TrailsLifeBioApplied1LectureBBothy
80586Machine learning and applications for biological data analysisLifeBioApplied1LectureBBothy
80665Medical InformaticsLifeMedicalApplied1LectureBBothy
80672Advanced Tools to Genome AnalysisLifeBioApplied1LectureABoth
80675Clinical Informatics - Clinical Data MiningLifeBioApplied0.5LectureABoth
80724Python Programming for Scientific ResearchLifeBioApplied1LectureBBoth
80725Deep Learning and Artificial Intelligence in MedicineLifeMedicalApplied1LectureBBoth
81936Digital Image ProcessingMedicineImageApplied1LectureBGrady
81958Text Mining for Cancer ResearchMedicineMedicalApplied1LectureAGrady
83003MATLAB programming and applicationsEngineeringProgrammingAppliedLabBUndergrady
83011Workshop in Python ProgrammingEngineeringProgrammingCore1LectureBBothy
83214Tools for Numerical AnalysisEngineeringMathCore1LectureBUndergrady
83216Introduction to Statistics and ProbabilityEngineeringStatistics/ProbablityCore1.5LectureAUndergrady
83223Object Oriented ProgrammingEngineeringProgrammingCore1LectureAUndergrady
83224Data Structures and Algorithms IIEngineeringAlgorithms/Data StructuresCore1.5LectureBUndergrady
83245Signals and SystemsEngineeringSignal ProcessingCore1.5LectureBUndergrady
83302Random Signals and NoiseEngineeringSignal ProcessingCore1.5LectureAUndergrady
83320Digital Signal Processing IEngineeringSignal ProcessingCore1.5LectureBUndergrady
83321Statistical Algorithms for Signal ProcessingEngineeringSignal ProcessingCore1.5LectureBUndergrady
83412Genetics and Molecular BiologyEngineeringBioApplied1LectureBUndergrady
83414Biological data scienceEngineeringBioApplied1.5LectureBUndergrady
83420Statistical Analysis of DataEngineeringStatistics/ProbablityCore1.5LectureBUndergrady
83456Design and Analysis of AlgorithmsEngineeringAlgorithms/Data StructuresCore1LectureAUndergrady
83459Software EngineeringEngineeringProgrammingCore1LectureBUndergrady
83620Information TheoryEngineeringMathCore1LectureABothy
83622Introduction to Machine LearningEngineeringML/DL/NNCore1LectureBBothy
83623Models and Mathematical Analysis of NetworksEngineeringSignal ProcessingCore1LectureABothy
83624Digital Signal Processing IIEngineeringSignal ProcessingCore1.5LectureABothy
83629Digital Image ProcessingEngineeringImageCore1LectureBBothy
83633Digital Geometric Processing IIEngineeringBioApplied1LectureABothy
83635Reinforcement-based learningEngineeringML/DL/NNCore1LectureABothy
83641Shape Optimization & UnderstandingEngineeringGeometryCore1LectureBBoth
83643Machine learning theoryEngineeringML/DL/NNCore1LectureABothy
83656Digital Processing of GeometryEngineeringGeometryCore1LectureBBothy
83665Computational BiologyEngineeringBioApplied1LectureABothy
83666Control ofTheory for Biological SystemsEngineeringBioApplied1LectureABothy
83674Quantum machine learningEngineeringQuantomCore1LectureBBothy
83676Data MiningEngineeringData Mining / VisualizationApplied1LectureBBothy
83692Social networksEngineeringNetworksCore1LectureBUndergrady
83805Continuous and Combinatorial OptimizationEngineeringOptimizationCore1LectureATeacher Certificationy
83806Random ProcessesEngineeringStatistics/ProbablityCore1.5LectureBGrady
83807Quantum ComputingEngineeringQuantomCore1.5LectureAGrady
83819Unsupervised LearningEngineeringML/DL/NNCore1LectureBBothy
83841Statistical Machine LearningEngineeringML/DL/NNCore1LectureAGrady
83843deep generative modelsEngineeringML/DL/NNCore1LectureAGrady
83867Probabilistic Methods and AlgorithmsEngineeringAlgorithms/Data StructuresCore1LectureBGrad
83876Decision Support Systems in medical imagingEngineeringMedicalApplied1LectureBGrady
83880Seminar/Advanced Topics in Signal ProcessingEngineeringSignal ProcessingCore1LectureBGrady
83881Digital speech processingEngineeringSpeechCore1LectureBGrady
83882Deep LearningEngineeringML/DL/NNCore1LectureAGrady
83887Spatial Signal ProcessingEngineeringSignal ProcessingCore1LectureBGrad
83888Computer VisionEngineeringImageCore1LectureBGrady
83889Advanced Topics in Statistical Signal ProcessingEngineeringSignal ProcessingCore1LectureAGrady
83900Discovery TheoryEngineeringMathCore1LectureAGrady
83901Introduction to Data Science with PythonEngineeringML/DL/NNApplied1LectureBGrad
83905Seminar/Advanced Topics in Machine Learning and Data ProcessingEngineeringML/DL/NNCore1LectureAGrady
83906Independence-based Blind Source SeparationEngineeringStatistics/ProbablityCore#N/A#N/AGrady
83907Advanced topics in deep learningEngineeringML/DL/NNCore1LectureBGrady
83908Advanced topics in differential privacyEngineeringPrivacyCore1LectureBGrady
83920Parallel Computation using a GPUEngineeringAlgorithms/Data StructuresCore1LectureAGrad
83979Statistics and Data AnalysisEngineeringData Mining / VisualizationApplied1LectureBGrady
83983Process Modeling and MiningEngineeringData Mining / VisualizationCore1LectureBBothy
84107Statistics and Probability for Chemists (SPC)ChemistryStatistics/ProbablityApplied1LectureAUndergrady
84190Introduction to Computers in ChemistryChemistryProgrammingApplied1LectureBUndergrad
84291Introduction to programming in Python for chemistryChemistryProgrammingApplied1LectureBBothy
84328Computational ChemistryChemistryBioApplied1LectureBUndergrady
84846Introduction to CheminformaticsChemistryBioApplied1LectureAGrady
86156Probability and Statistics for PhysicistsPhysicsStatistics/ProbablityApplied1.5LectureBUndergrady
86164Introduction to Computers in PhysicsPhysicsProgrammingApplied1LectureAUndergrady
86605Data science for physicistsPhysicsML/DL/NNApplied1.5LectureABothy
88151Computer Applications in MathMathProgrammingCore1LectureBUndergrad
88153Introduction to Mathematical ProgrammingMathProgrammingCore1LectureBUndergrad
88165Introduction to Probabilitiy and StatisticsMathStatistics/ProbablityCore2LectureBUndergrady
88170Introduction to ComputingMathProgrammingCore1.5LectureAUndergrady
88174Introduction to Object Oriented ProgrammingMathProgrammingCore1LectureBUndergrady
88263Introduction to StatisticsMathStatistics/ProbablityCore2LectureAGrady
88280Data Structures and AlgorithmsMathAlgorithms/Data StructuresCore2LectureAGrady
88584Image ProcessingMathImageCore1LectureABothy
88615Introduction to Probabilitiy and StatisticsMathStatistics/ProbablityCore1LectureBBoth
886210Risk management and time seriesMathFinance/EconometricsCore1LectureSummerBoth
88622Introduction to Probabilitiy and StatisticsMathStatistics/ProbablityCore1.5LectureAGrady
88623Probability and Stochastic ProcessesMathStatistics/ProbablityCore1.5LectureBGrady
88624Analysis of Statistical DataMathData Mining / VisualizationCore1LectureSummerBothy
88631Introduction to Probabilitiy and StatisticsMathStatistics/ProbablityCore0.5LectureABothy
886788Data Science SeminarMathML/DL/NNCore1SeminarBGrady
886960Introduction to Programming using pythonMathProgrammingApplied1LectureBUndergrady
886961Python Programming WorkshopMathProgrammingApplied1LectureABothy
886970Data Processing, Analysis and VisualizationMathData Mining / VisualizationApplied1LectureBUndergrady
886971Applied Machine LearningMathML/DL/NNApplied1LectureABothy
886972Big DataMathDB/Big DataApplied1LectureABothy
886980Networks and Complexity in the Real WorldMathNetworksApplied1LectureBGrady
88760Introduction to StatisticsMathStatistics/ProbablityCore1LectureAGrady
88761Introduction to Statistics IIMathStatistics/ProbablityCore1LectureBGrady
88775Statistical TheoryMathStatistical TheoryCore1.5LectureAGrady
88778Network ScienceMathNetworksCore1.5LectureBGrad
88779Random graphs and networksMathNetworksCoreLectureBGrad
88780Supervised and Unsupervised LearningMathML/DL/NNCore1LectureAGrady
887810Introduction to artificial intelligenceMathAICore1LectureBBothy
88784OptimizationMathOptimizationCore1.5LectureABothy
88962Probability and Stochastic ProcessesMathStatistics/ProbablityCore1.5LectureAUndergrady
89110Introduction to Computer ScienceComputer ScienceProgrammingCore1.5LectureAUndergrady
89111Introduction to Object Oriented ProgrammingComputer ScienceProgrammingCore1LectureBGrady
89120Data StructuresComputer ScienceAlgorithms/Data StructuresCore1LectureBBothy
89210Algorithmic Programming IComputer ScienceAlgorithms/Data StructuresCore1LectureABothy
89211Algorithmic Programming IIComputer ScienceAlgorithms/Data StructuresCore1LectureBGrady
89220Algorithms 1Computer ScienceAlgorithms/Data StructuresCore1.5LectureABothy
89255Graph TheoryComputer ScienceNetworksCore1.5LectureABothy
89262General ProbabilityComputer ScienceStatistics/ProbablityCore1LectureABothy
89264BiostatisticsComputer ScienceBioCore1.5LectureABothy
89276Numeric MethodsComputer ScienceMathCore1LectureBUndergrad
89312Programming in a multi-processor environmentComputer ScienceProgrammingCore1LectureBBothy
89322Algorithms 2Computer ScienceAlgorithms/Data StructuresCore1LectureBBothy
89350Introduction to Communication NetworksComputer ScienceNetworksCore1LectureABothy
89362General StatisticsComputer ScienceStatistics/ProbablityCore1LectureBBothy
894043Advanced Seminar in natural lagquage processingComputer ScienceNLP/TextCore1SeminarBBothy
894044Research seminar in natural language processing – part 2Computer ScienceNLP/TextCore1SeminarBUndergrady
89408Advanced Seminar in Algorithmic Game TheoryComputer ScienceAlgorithms/Data StructuresCore1SeminarBUndergrady
894112User Behavior Machine Learning Algorithms SeminarComputer ScienceML/DL/NNCore1SeminarBUndergrady
89421Seminar/Strategic Planning for RobotsComputer ScienceRoboticsCore1SeminarBUndergrad
894483Seminar in Machine Learning and Speech ProcessingComputer ScienceSpeechCore1SeminarBUndergrad
89452Seminar/Web, Crowd and Big Data ManagementComputer ScienceDB/Big DataCore1SeminarAUndergrady
894531Seminar: Learning Algorithms and Natural Language ProcessingComputer ScienceNLP/TextCore1SeminarBUndergrady
89454Structures for SemanticsComputer ScienceNLP/TextCore1SeminarAUndergrad
89460Seminar: From Text to InformationComputer ScienceNLP/TextCore1SeminarBUndergrad
89471Seminar in Pattern RecognitionComputer ScienceImageCore1SeminarAUndergrad
894851Advanced Seminar in Text UnderstandingComputer ScienceNLP/TextCore1SeminarBUndergrad
89493Seminar/Applied Machine Learning for NLPComputer ScienceNLP/TextCore1SeminarBUndergrady
89511Machine LearningComputer ScienceML/DL/NNCore1LectureABothy
89512Computational BiologyComputer ScienceBioApplied1LectureBBothy
89519Machine Learning for HealthcareComputer ScienceMedicalApplied1LectureBBothy
89542Management of Big Web DataComputer ScienceDB/Big DataCore1LectureABothy
89560Image ProcessingComputer ScienceImageCore1LectureABothy
89570Artificial IntelligenceComputer ScienceAICore1LectureABothy
89581Database SystemsComputer ScienceDB/Big DataCore1LectureBBothy
89594Formal Representations for Natural LanguagesComputer ScienceNLP/TextCore1LectureBBothy
89608Speech RecognitionComputer ScienceSpeechCore1LectureBBoth
89641Topics in Information TheoryComputer ScienceMathCore1LectureBBothy
89654Advanced Methods in Machine LearningComputer ScienceML/DL/NNCore1LectureBBoth
896541Practical topics in Machine LearningComputer ScienceML/DL/NNApplied1LectureBBoth
89679Workshop in DatabasesComputer ScienceDB/Big DataCore1WorkshopABothy
89680Natural Language ProcessingComputer ScienceNLP/TextCore1.5LectureBBothy
89685 Introduction to RoboticsComputer ScienceRoboticsCore1LectureABothy
89687Deep Learning Methods for Texts and SequencesComputer ScienceNLP/TextCore1LectureBBothy
896871Deep learningComputer ScienceML/DL/NNCore1LectureABothy
896872Deep Neural Network for Computer VisionComputer ScienceML/DL/NNCore1LectureBBoth
896873Reinforcement LearningComputer ScienceML/DL/NNCore1LectureABothy
896874Deep learning for perceptionComputer ScienceProgrammingCore1LectureAGrad
89688Statistical Machine TranslationComputer ScienceNLP/TextCore1LectureBBothy
89919Applied Probabilistic Models in Computer ScienceComputer ScienceML/DL/NNCore1LectureABothy
89950Advanced Topics in Artificial IntelligenceComputer ScienceML/DL/NNCore1LectureBBothy
89991Machine Learning ColloqiumComputer ScienceML/DL/NNCoreColloquiumAnnualBothy
996728Law and Big DataLawData Mining / VisualizationApplied1LectureBBothy
999025Data Science for JuristsLawData Mining / VisualizationExposure1.5LectureABothy

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:

  1. Optimize the integrated hardware system for various use-cases, taking into account dependencies and interactions between algorithms, software, drivers and hardware.
  2. 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.
  3. 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).
  4. 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.
  5. Support of porting and optimization of existing HPC code to a GPU setting.
  6. 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 changeprotection of labour rights across global supply and commodity chainsglobal 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.