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Field
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and fine-tuning frameworks would be welcome. Proficiency in software engineering. A good understanding of data structures and algorithms and data-oriented approaches to problem-solving. Excellent
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, Hugging Face etc., in applied problem-solving contexts. Understanding of machine learning algorithms (gradient descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers
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, transcriptomics (bulk, scRNAseq) epigenetic profiling (methylome, Atac-Seq, Chip-seq) and proteomics, using computational methods. Algorithm Development: Designing and implementing algorithms to process, analyse
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document complex software applications. Ability to examine the details of complex algorithms. Demonstrated ability to work effectively, both independently and collaboratively, with a strong commitment to
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/experience areas include: A Master’s degree in Electrical Engineering, Computer Engineering, or Mechanical Engineering GCC/Eclipse based toolchains Linux and embedded real-time operating systems DSP algorithm
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and implement generalizable algorithms and tools for analysis of biological data, including high-throughput functional genomics assays Evaluate and recommend new emerging technologies, approaches, and
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and existing tools to interpret, analyze, and visualize multivariate relationships in data. Create databases and reports, develop algorithms and statistical models, and perform statistical analyses
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. Possible teaching areas include introductory programming in Python, data structures and algorithms, database systems and data management, cloud computing for data science, advanced data visualization, and
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, computers and instrumentation hardware. Ability to install and oversee installation software algorithms for distributed controllers and maintain associated databases. Ability to coordinate maintenance and
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-scale data sets, especially using performant algorithms. Develop machine learning approaches, computer vision tools to help pre-process dataset and annotations to generate groundtruth benchmarks