Data Science in Nuclear Verification - Extracting Verification-relevant Information from Geospatial Big Data
I would like to benefit from an extensive transferrable skill program of leading researchers to pursue my interest in the fascinating and challenging field of data science especially in image analysis and to exchange ideas with other PhD students in an interdisciplinary environment.
Process-Inspired Data Acquisition for Autonomous Ice Exploration Robots on Earth and Beyond
I found a challenging project that puts into practice my knowledge of mathematical modeling, numerical simulation and data analysis.
Scenario generation for optimization-based design and operation of multimodal energy systems under uncertainty
Prof. Alexander Mitsos, Ph. D.
I like the idea of building connections between different fields of research through common methods and idea.
Optimal data-driven models for optimization-based design of energy systems
Prof. Alexander Mitsos, Ph. D.
I want to be on the forefront of scientific and technological developments.
Machine learning-based forecasting and scheduling optimization for energy systems under uncertainty
Prof. Alexander Mitsos, Ph. D.
additionally to the interesting trainings, courses and seminars about latest research topics, I enjoy the exchange within the interdisciplinary group of young, motivated researchers very much.
Machine Learning and Bayesian Methods in Neuroscience
It enables an intensive exchange of experiences and concepts in data science across various research communities. Thereby, it provides new perspectives and inspires new approaches for research and applied problems. At the same time, HDS-LEE offers PhD students in-depth training ranging from data engineering topics, such as data versioning, to method-related topics, such as hyperparameter optimization in deep learning.
View an article about his research at www.helmholtz-hida.de
Predicting the flow in patient-specific aneurysm geometries using convolutional neural networks
I believe that helping people is the most rewarding effort in life and my work in the program contributes a lot to it.
Structure identification of mesoscopic biological interaction networks from data
it allows me to study complex systems on a highly interdisciplinary environment. The projects are cutting edge and there is a strong emphasis on the development of advanced computational skills, essential in any field of applied science.
Hybrid process modeling combining mechanistic transport equations with machine learning for thermodynamic equilibria
of my interest towards data science. I come from a background of applied mechanical engineering and in the past projects I have been working on the optimization problems that gave me motivation to further dive deep into this field. The current project provides me with an opportunity to explore the different aspects of machine learning on a very complex physical phenomenon and I hope by the end of this project I will be able to apply these techniques on various other applications.
Quantitative strain library phenotyping and bioprocess optimisation enabled by laboratory automation, process modelling and microscale cultivation
I want to broaden my knowledge on data science and exchange ideas with other young data scientists in an interdisciplinary environment.
Prediction of magnetic materials for energy and information combining data-analytics and first-principles theory
I believe that interdisciplinary collaboration in science will give rise to many innovations in the future and I would like to discover the possibilities which modern data analysis techniques offer in modern research.
Statistic and data-based methods for energy system analysis and modelling
Prof. Dr. Astrid Kiendler-Scharr
I would like to understand how we can implement and operate future renewable energy systems that are governed by different heterogeneous time series and their complex interactions.
An approach to tracking the provenance of data acquisition and analysis in neuroscience
I want to get training in data science in the context of my main scientific interest: neuroscience.
Standardised data acquisition in biocatalysis according to the FAIR principles
I want to get the opportunity to learn about data science from leading scientist and meet other PhD students working in this field.
Exploitation of street canyon observations for inner city air quality forecasts and emission optimization
Prof. Dr. Astrid Kiendler-Scharr
it offers a great opportunity to actively participate in a new development in science in connection with politically relevant topics.
Land surface-subsurface modelling and data assimilation over Africa
Prof. Dr. Harrie-Jan Hendricks Franssen
of the opportunity it offers to advance my knowledge in my chosen career path. While theoretical knowledge advancement is expected, HDS-LEE positions itself to develop a "total researcher" through the availability of soft-skills trainings, seminars and retreats that develop not only academic life but social life as well.
Performance of Dense and Sparse Tensor Operations in Applications
I want to focus on interesting inter-disciplinary problems and to work together with people from different research fields.
Data-Driven Analysis of Medical and Simulation Data for Improved Patient Treatment in the Human Respiratory System
Prof. Dr.-Ing. Wolfgang Schröder
I get the chance to network with researchers from different fields but with similar challenges.
View an article about his research at www.helmholtz-hida.de
Geometry-aware image analysis for microfluidic live-cell experimentation
I am obsessed with data science and I would like to know more about different applications of it in image processing.
Prediction of ligand protein interactions and brain signalling cascades using machine learning approaches: olfaction as a test scenario.
Jun-Prof. Dr. Mercedes Alfonso Prieto
taking into account my previous experiences, trainings and desire for the near future, in my opinion HDS-LEE program will fit perfectly in my background and with my goals. Moreover, the program is shaped so as to give me the opportunity to improve in my field and to learn very useful notions of related fields, helping me to become a well-rounded scientist.
Hybrid Data Assimilation for Applications in Cryosphere Physics
I want to benefit from the trainings and the interdisciplinary supervision provided by the graduate school to advance my research.
Uncertainty Estimation of Deep Neural Network Predictions
It allows me to research at the intersection between Data Science and the three future-oriented domains Life, Earth and Energy.
Binding Interface Classification Employing Image Recognition
Jun.- Prof. Dr. Giulia Rossetti
the research topic is highly interesting and on the forefront of technical development in the data science community.
Neural Networks as reduced models for high-fidelity flow simulations in bioreactors and energy-efficient manufacturing processes
I want to enhance my knowledge of simulation with data science techniques and to work with other researchers from different fields in a friendly and collaborative atmosphere.
Meta-learning and hyper-parameter Optimization in Artificial and Biological Networks
I like to work in an interdisciplinary field. The HDS-LEE gives me an opportunity to meet and discuss with scientists with different backgrounds. This helps me to enrich my knowledge in the data sciences and to advance my own work. Additionally, the HDS-LEE offers a variety of courses, seminars and workshops to develop soft-skills.
Probabilistic Simulation and Uncertainty Quantification for Earth Surface Processes
I want to learn about advanced data science methodologies and technologies from leading researchers.
Model-based Time Series Analysis for reliable Energy System (MBTSARES)
