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Field
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, and Lifelines. Groups of patients with different prognoses will be identified through unsupervised clustering using algorithms such as K-means and NMF. To evaluate the tumor microenvironment, tools
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: Neural networks and machine learning. Algorithm. Professional Experience: In the use of Python (PyTorch, TensorFlow) and C for the development and optimization of deep learning algorithms. Experience in
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-including evolutionary algorithms, ant colony optimisation, and simulated annealing-to fine-tune an LLM/agent that generates high-quality prompts, inputs, and tool-use strategies for density functional theory
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optimiser that accelerates both workflow efficiency and materials discovery. Main Tasks and responsibilities: Own the optimiser: design, implement, and tune heuristic/metaheuristic algorithms (e.g
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background in computer science or biomedical engineering, with a strong focus on programming using deep learning libraries and machine learning algorithms. Demonstrated experience in medical image processing
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analysis. Methods and algorithms including visualisation and reporting tools. Geotechnical, structural and mechanical characterisation of rock from drilling signals. Calibration and interpretation on rock
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site-specific and realistic radio propagation data through GPU-accelerated ray tracing to train AI/ML algorithms. Exploring the use of generative models for wireless channel modeling, e.g., to produce
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their creation. Functions to be developed: Build a Reinforcement Learning system that supervises and adjusts text generation through inputs and outputs. Develop automatic model selection algorithms
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processes with a focus on atmospheric applications. Contribute to the development and implementation of mathematical models and numerical algorithms. Analyze data from numerical simulations, climate models
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: Artificial Intelligence Models. Deep Learning. Development and implementation of the Model Predictive Path Integral algorithm: MPPI. Professional Experience: Development of perception and localization systems