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Field
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field. A background in organic chemistry, and/or high-throughput experimentation is desirable. Proficiency in programming languages (Python/MATLAB) commonly used in machine learning applications is
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field. A background in organic chemistry, and/or high-throughput experimentation is desirable. Proficiency in programming languages (Python/MATLAB) commonly used in machine learning applications is
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programming tools (e.g., Python, R, or MATLAB), ideally with experience working with spatiotemporally explicit datasets in raster or NetCDF formats. Familiarity with optimisation techniques such as mixed
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in Python programming. Experience with machine learning methods, bioinformatics, and data science. Familiarity with generative AI tools for protein design and protein language models. Knowledge
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dynamics simulations. The project will entail programming (scripting, Python etc.), handling big data and carrying out all-atom and coarse-grained molecular dynamics simulations on high-performance super
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Python programming. Application procedure The application should be written in English be attached as PDF-files, as below. Maximum size for each file is 40 MB. Please note that the system does not support
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analysis of large data sets, statistical modeling, and knowledge of at least one programming language (e. g.: R, Python and/or Julia) are required. Experience in machine learning and image recognition
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experience with algorithms relevant to computational biology documented programming skills, e.g. in Python and R very good communication and organizational skills with the ability to work to timelines, both
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of the (computational) mechanics of solids and the finite element method and/or spectral solvers Practical experience in at least one programming language (preferably Python) and experience with the use of Unix/Linux
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chemistry) laboratory; statistical skills for proper data treatment ; experience with programming (R or Python) for data handling and visualisation; strong analytical skills; excellent communication skills