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Machine Learning Integration Develop and implement machine learning algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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around 15 are PhD students. The work environment is open and welcoming, striving to provide each employee with the opportunity to develop personally and professionally. The field of solid mechanics relates
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collaboration with other researchers. Your profile • Formal requirement: Doctoral degree (PhD) in quantitative genetics, tree breeding, biometrics, statistical genetics, bioinformatics or related field
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman
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Description of the workplace The position is at the Department of Computer Science within a growing research group in foundations of computer science at Lund University. We expect to have 8 PhD
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). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction solvers. Computational aeroacoustics. Swedish is not required
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of computational fluid dynamics (CFD). Knowledge of finite element method (FEM). Meritorious: It is also an advantage if you have experience with: Machine learning. Coupling algorithms of fluid-structure interaction
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110 PhD students. The Department of Oncology-Pathology is responsible for undergraduate courses in Pathology, Oncology and Forensic Medicine for medical students, as well as for Tumor biology courses
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computational algorithms to enable regenerative cell therapies. Now, we are seeking a highly driven postdoctoral researcher to contribute to our ambitious mission. Division The Division of Biomolecular and