Applied Deep Learning Scientist, Geometric Deep
vor 2 Wochen
What you will be doing:
- Prototype and build deep learning algorithms for graph and geometric deep learning in the biological sciences- Design metrics for and assist with the evaluation of model predictions and results- Know the latest research and identify ways to capitalize on new advancements, either as applied research projects or by directly integrating into product development- Collaborate with multiple AI infrastructure and research teams- Find opportunities to incorporate advances in the field and other NVIDIA products into our infrastructure
What we need to see:
- 5+ years of relevant experience- Completed a MS or PhD Degree in a quantitative field such as Statistics, Physics, Computational Biology, Computer Science, Mathematics (or a related field), or equivalent experience- Expertise in deep learning and machine learning- Strong experience with Python for deep learning (PyTorch, TensorFlow, Jax, Warp) and relevant specialized deep learning libraries (e.g. PyG, DGL, e3nn)- Experience with modeling and validation of protein sequences and/or protein structures and related tools, such as biopython, mmseqs2, jackhmmer, PyMol- Recognition for technical leadership contributions, capable of self-direction, and willingness to learn from and teach others- You should display strong communication skills, be organized and self-motivated, and play well with others (be a phenomenal teammate)
Ways to stand out from the crowd- Familiarity with latest advances in geometric and/or generative deep learning models in biological sciences, such as AlphaFold and DiffDock- Experience with protein or small molecule simulation tools that use atomistic or coarse grained interaction models such as OpenMM and GROMACS- Experience with software and tools for modeling and validation of cheminformatics and protein structural data, such as RDKit, Biopython, and PyMol- C/C++, CUDA, docker- Knowledge of open source development- Relevant publication history and/or conference attendance
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