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microbiome studies, microfluidics, or/and Machine-Learning to join our dynamic research team. The successful candidate will play a pivotal role in advancing our understanding of microbial communities through
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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Qualifications: Knowledge of kinetic modeling and/or machine learning interatomic potentials. Background Investigation Statement: Prior to hiring, the final candidate(s) must successfully pass a pre-employment
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standards and data protection regulations. This includes managing sensitive data correctly and guaranteeing that machine learning applications are developed with ethical considerations in mind. Participate in
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-Coding Genome Edits: Develop innovative machine learning approaches for designing precise non-coding genome edits, focusing on how non-coding alterations influence gene regulation and cellular function
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environments for statistical analysis (e.g., MATLAB, R, or Stata); · Creating and managing very large datasets; · Machine learning skills. Basic Qualifications A Ph.D. in any business discipline
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& Computer Engineering Work Location EPIC Vacancy Open To All Candidates Position Designation Post Doc Employment Type Temporary - Part-time Hours per week 40 Work Schedule Pay Rate $48,000 - $53,000 Minimum
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surface science. Preferred Qualifications: Knowledge of kinetic modeling and/or machine learning interatomic potentials. Background Investigation Statement: Prior to hiring, the final candidate(s) must
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People from Deepfakes Project. We are looking for a software/machine learning engineer (or similar) to work in an interdisciplinary team reporting to Dr Sophie Nightingale (Principal Investigator
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two-year research project in collaboration with international academic partners. The main objective of the project is to study the interaction between machine learning and wireless communication fields