Data Science is an interdisciplinary field focusing on methodologies for extracting knowledge and insights from data thus contributing to different areas of science. The Focus Area builds on the pioneering scientific results of the community and paves way for new breakthrough results across disciplines. Data science researchers concentrate on the analysis of complex, heterogeneous, and distributed large-scale data. Data analysis environments vary from high-performance computing systems, cloud environment, to edge and IoT systems. Our research methods are data, system and application driven, and our research areas vary from the genome level, industrial engineering processes, software systems, networks in human societies to astrophysical phenomena. The overarching goal of the Focus Area is to leverage the synergies of the network in solving significant societal and industrial challenges related to data analysis.
Members of our larger Helsinki ICT community also provide funding for researchers as Postdoctoral Researchers. Researchers in these positions are hired to work on self-funded projects or in externally funded research groups. More information about these self-funded projects and research groups are listed below.
Bridging the gap between large heterogeneous data and trustworthy research in the social sciences and humanities (BRIDGE)
This project aims to develop more epistemologically sound research processes for data-centric research in the digital humanities and computational social sciences. Work in the project starts from three epistemic validity -threatening problems that the applicant has identified to plague much research in the domain. While different, these three all arise from the non-standard and heterogeneous nature of the large datasets of interest in such research. They are: 1) errors, gaps, bias and representativeness issues in the source data, 2) the abundance in the data of complex interactions and confounding factors and 3) a gap existing between what is in the data and the object of interest.
In the proposal, these problems are proposed to be tackled by a multi-pronged approach that will utilize approaches from multiple fields of computer science, statistics and beyond to unearth and correct for the issues identified: formal Bayesian modelling, other more informal means of capturing uncertainty, interactional support and process design, as well as algorithmic data-mining approaches such as clustering and anomaly detection.
Explainable and robust AI for scientists
Machine learning and AI are extensively used in the sciences. When modelling physical systems, understandability and statistical robustness of the models is often more important than predictive accuracy. We are looking for talented postdoctoral researchers and doctoral students to study explainable and understandable AI and to study the uncertainty quantification of the AI models. While the AI methods we develop are generic and not tied to any specific application domain we work closely with scientists to build Virtual Laboratory for Molecular Level Atmospheric Transformations. More information: https://bit.ly/edahelsinki2022jobs
Machine Learning for Health (ML4H)
Accumulation of health data has enabled researchers to study questions such as: how to accurately predict the risk of disease, how to provide personalized treatment based on real-time patient data, or how to use genomic data to understand disease or antibiotic resistance. Central challenges in ML4H include noisy data, multiple heterogeneous data sources, learning about causality, interpreting the models, and assessing the uncertainty of predictions, to name a few. We tackle these by developing models and algorithms which leverage modern machine learning principles: Bayesian neural networks, deep latent variable models, Gaussian processes, causal inference, transformers, reinforcement learning, and natural language processing. Successful applicants are expected to have outstanding skills in machine learning, statistics, applied mathematics, or a related field, and the topic may be adjusted based on the applicant’s interests to either methodology or applications; examples of both can be found in https://users.ics.aalto.fi/~pemartti/.
Modeling and machine learning in precision oncology
Cross-disciplinary research group of Prof. Sampsa Hautaniemi aims at discovering causes for drug resistance in cancer patients and effective treatment options to overcome drug resistance using mathematical modeling and machine learning. We have strong track-record in modeling and computational method development (e.g., https://pubmed.ncbi.nlm.nih.gov/33720334/, https://pubmed.ncbi.nlm.nih.gov/35196078/ & https://pubmed.ncbi.nlm.nih.gov/29769198/) as well as translating research results to clinical practice (e.g., https://ascopubs.org/doi/full/10.1200/PO.18.00343). We coordinate one of the largest ovarian cancer precision oncology projects in the world funded by EU Horizon 2020 (DECIDER; https://www.deciderproject.eu/). In DECIDER our focus is to discover effective treatment options for ovarian cancer patients who do not respond to chemotherapy anymore. More information and all our publications are available at our group website: https://www2.helsinki.fi/en/researchgroups/systems-biology-of-drug-resistance-in-cancer
We are looking for post-doctoral researchers with strong experience in mathematical or statistical modeling, machine learning and interest in using real-world cancer patient data in modeling and analysis. Basic knowledge of cancer biology or genetics is not required but is considered as a plus. Examples of the possible projects are: mathematical modeling tumor evolution during treatment using longitudinal data; causal analysis of single-cell data; development and use of Deep Learning methods in histopathological images/genomics data; multi-omics integration.
The Hautaniemi group belongs to the Research Program in Systems Oncology (ONCOSYS; https://www.helsinki.fi/en/faculty-medicine/research/research-programs-unit/systems-oncology-oncosys). ONCOSYS consists of internationally acclaimed basic, translational and clinical researchers who share the vision of using cutting-edge measurement technology, real-world data and AI in cancer research in order to discover effective diagnostic, prognostic and therapeutic approaches. If you enjoy doing science in an interdisciplinary and collaborative environment, consider applying to us.