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, or computational biology Proficiency in Python and experience working in Linux-based HPC environments or cloud computing platforms Proven experience with deep learning frameworks such as PyTorch or TensorFlow, and
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
for isomerization in MOFs: https://doi.org/10.1039/D3QI01065A What we are looking for: A PhD in Chemistry, Materials Science, Physics or a related field Experience in python (ASE, scikit-learn, pytorch) is essential
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, including fitting and manipulation of large array-type data sets (using Python, Matlab or equivalent) Ability to communicate well, and work within a collaborative team environment Preferred Knowledge, Skills
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. Experience with large-scale omics data analysis and programming (R or Python). Proven track record of high-quality publications. Experience with molecular biology techniques (e.g., cell culture, CRISPR, NGS
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publications), and or other textual data Knowledge of, or strong interest in AI, digital technologies, and their applications Proficiency in programming languages (e.g., Python, R) and data analysis Ability
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Environmental Engineering or a related field High proficiency in programming languages (e.g., Python or MATLAB) A strong record of authorship in peer-reviewed academic journals Excellent interpersonal and
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(Python or similar) Prior experience in skin biology or inflammatory diseases is an advantage Excellence in both written and spoken English Passion for collaborative and interdisciplinary research Research
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learning methodologies. The underlying data are complex and will require sophisticated data management and integration skills. A candidate should have proficiency with GIS software and Python, strong written
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.). Strong programming skills in Python, C/C++, and experience with data-centric control and monitoring architectures. Knowledge on standard communication protocols for smart grid interoperability, such as IEC
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, demonstrated experience of coding in programming languages such as R and Python is considered particularly advantageous. Examples of computationally intensive methods central to IAS and IDA are data-driven text