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(e.g. R, Python) and an ability to work with large datasets Strong record of peer-reviewed publications Ability to independently design and execute experiments and interpret data Ability to work in a
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in programming, ideally with experience in languages commonly used in computational biology (e.g. Python, C). Experience with HPC, workflow managers (e.g. Snakemake, Nextflow), and containerization
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novel machine-learning methodologies Excellent programming skills in Python and familiarity with modern ML tooling and reproducible research practices Experience training and deploying machine-learning
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of molecular phenotypes, Mendelian Randomization, co-localisation); Experience of statistical or other programming languages to manipulate large-scale datasets – e.g. Python, R; Strong quantitative skills and
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learning models (e.g., PCA, PLS-DA, clustering, CNNs) to classify microplastic particles based on spectral and morphological fluorescence data. Develop and maintain modular analysis pipelines in Python
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Familiarity with omics approaches such as scRNA-seq, proteomics Programming or data analysis skills such as R, Python, or similar Experience in international collaborative projects Experience with laboratory
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experience in writing successful research proposals Experience in research project management or coordination Solid software development skills in Python; C/C++ is a plus Fluency in English is required
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qualifications: Applicants must hold a PhD degree in computer science, bioinformatics or similar. The applicant must be proficient in programming in Python and Java script. Have strong cooperation and
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, systems biology, biomedical data science, cancer biology, or a related field Strong experience in omics data analysis, ideally including single-cell or spatial transcriptomics Proficiency in R and/or Python
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applications Good analytical and (bio)statistical skills Knowledge of relevant programming languages such as Java, Python, and Perl Good knowledge of relational and document-oriented database design (e.g., MySQL