Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Field
-
(spoken and written). Preferred qualifications Prior experience with 3D cell culture, organoids, CRISPR-Cas9, or imaging-based phenotyping. Familiarity with transcriptomics or basic computational biology (R
-
: Research question and methodology Sources Two possible case studies (ca. 150 words each) A curriculum vitae Copies of (R)MA diploma and list of grades. Copy of (R)MA thesis, or another writing sample (e.g
-
studies (ca. 150 words each) A curriculum vitae Copies of (R)MA diploma and list of grades. Copy of (R)MA thesis, or another writing sample (e.g. an article, book chapter or other academic text that you
-
communication skills in English. Ability to work independently and collaboratively within an interdisciplinary research environment. Desirable Experience with statistical software such as Stata, R, SPSS, etc
-
, Python, and R. The candidate should have a strong capacity to understand processes underlying pro-environmental behaviour from different perspectives, enabling them to simultaneously understand, use, and
-
programming languages such as R (Experience with NetLogo is preferred but not essential). experience in the development and use of spatial microsimulation and a familiarity or willingness to learn agent-based
-
Psychology, Marketing, Business, Economics, Econometrics, Communication, Data Science, or a closely related field. You have experience with data analysis (e.g., R, SPSS, Stata, Python, SQL), web scraping, and
-
experience with data analysis (e.g., R, SPSS, Stata, Python, SQL), web scraping, and data management. Familiarity with open science practices (e.g., GitHub, database management) is a plus. You are eager
-
bioinformatics and data analysis (i.e. R, Python, Perl) is a significant plus. Organisation Conditions of employment We offer you in accordance with the Collective Labour Agreement for Dutch Universities (https
-
demonstrable experience in R, Python, or a related programming language. Has demonstrable experience working with (large) datasets. Is familiar with experimental methods (preferred, not required) Already has