Rolling Basis Applications
Early Stage Researcher (ESR8)
PhD fellowship in machine learning methods at the genome scale.
“E-MUSE Complex microbial ecosystems multiscale modelling: mechanistic and data driven approaches integration” MSCA-ITN-2020 European Training Network
University of Szeged (USZ), established in Dugonics Tér 13, Szeged 6720, Hungary
Machine learning on multi-omics data, feature selection, support mechanistic models with machine learning
Rolling Basis Applications
Envisaged Job Starting Date
The appointment will initially be for 1 year. After a satisfactory evaluation of the initial appointment, the contract will be extended for a duration of 2 years.
full-time employment (based on COVID-19 evolution and restrictions, possibility to start remotely, once situation allows the presence is required)
€2 530.98 Eur gross salary/month
+ €600 gross mobility allowance/month and €500 gross family allowance/month (if applicable)
Taxation and Social (including Pension) Contribution deductions based on National and company regulations will apply.
Net estimation €1800
The objective of this ESR project is to train young researchers with strong programming and/or data scientist background to tackle machine and deep learning problems in the field of systems biology and multi-omics. As an example, development of mechanistic models will be supported by machine learning, for instance by pinpointing inconsistencies in the models with the data and by identifying links between variables not captured in the models. Traditional machine learning approaches include unsupervised learning and clustering to reason about the network links and role in genome-scale models. Tree based methods (random forest, decision tree) provide a natural way to analyse the contribution of different features in a model. More recent sequence-based learning methods like recurrent neural networks are able to use contextual information. In these models there is no need for advance feature selection, since it is encoded in the weights of the network. Although the interpretability of these models is limited, in practice they perform well if sufficiently large amount of data is available. Word/sentence embedding methods are state of the art ways to represent sequential data to deep neural networks in analogue natural language processing tasks. In this project sequence embedding techniques are investigated as an input layer of neural networks. The machine learning models are utilized in dynamics modelling and process modelling tasks.
We expect to deliver biologically meaningful machine learning models to find hidden patterns in omics data and gain deeper insight in the complex interactions between microbial communities using a data-driven approach.
In total, 2 months will be spent at Vrije Universiteit Amsterdam in Netherlands to gain knowledge in mechanistic modelling.
Enrolment in Doctoral degree
University of Szeged (USZ) (https://u-szeged.hu/english)
Required Education Level
Master’s degree in computer science or equivalent degrees ideally with a strong background in data science, machine learning, deep learning no later than September 2021. You should NOT have any kind of PhD degree. Previous research experience (which must be no longer than 4 years) although appreciated, is not mandatory.
Skills / Qualifications
Computer science, data science, machine learning, deep learning background
Provable software development skills in one of common programming languages (e.g. Java, C++)
Python, Tensorflow, PyTorch experience is favoured
Willingness to learn the necessary biology background in order to work with biological data properly
Educational background and previous research experience relevant for the chosen position
Networking and good communications skills (writing and presentation skills)
Willingness to travel abroad for the purpose of research, training and dissemination
English: B2, good oral and written communication skills in English are compulsory
The Univeristy of Szeged has vast knowledge and experience in various fields of machine learning and deep learning. The Research Group on Artificial Intelligence, at which the proposed work will be carried out mainly focuses on image, voice and text based AI research, including medical AI applications and cutting-edge AI solutions for international industry partners.
Prof. Dr. Gyimóthy Tibor is corresponding member of the Hungarian Academy of Sciences, professor at the Software Engineering Department at the University of Szeged in Hungary and the head of the MTA-SZTE Research Group on Artificial Intelligence. His research interests are concentrated on high quality software development and artificial intelligence applications. He supervised 15 PhD students (2 ongoing). He is the head of research in an R&D team of 150 researchers and developers.
Dr. László Vidács is a senior research fellow, deputy head of MTA-SZTE Research Group on Artificial Intelligence with 6 researchers with PhD, 4 PhD students and 8 young researchers, while his research is strongly connected to the Department of Software Engineering, University of Szeged. He is a supervisor of 4 ongoing PhD and 6 Masters students. He has leading role in 11 EU funded and industrial R&D projects.
Host Institution Description
The University of Szeged has been one of the best universities in Hungary for years, according to the international QS World University Rankings. Since Hungary is a member of the European Higher Education Area (EHEA), it also has EU accreditation. It is a research university with 12 Faculties, covering 700 research areas at 19 Doctoral Schools. Outstanding professors have worked at the university, including the Nobel Laureate Albert Szent-Györgyi (1937), who was the first to isolate Vitamin C, extracting it from Szeged paprika. Our student body grew to 21000, in which the number of international students exceeds 4000 coming from 115 countries. Artificial Intelligence research is mainly concentrated in three research units: the Department of Software Engineering, the Department of Algorithms and Artificial Intelligence and the Research Group on Artificial Intelligence.
Apart from quality education these groups have also been doing high impact academic research on various fields of AI including speech technology, natural language processing, software engineering, security, deep learning, self-organizing systems and theory and methodology of machine learning. Applications include several healthcare domains where state-of-the-art NLP and deep learning based image processing technologies are applied. USZ has active research in analyzing deep learning algorithms and examining the adversarial robustness of machine learning algorithms.
The University is located in the sunniest city of Hungary attracting thousands of young people due to its lively, urban lifestyle and colourful festivals. More information on student’s life in Szeged:
• Welcome to the University of Szeged – The First Impressions
• Education at the University of Szeged (https://www.youtube.com/watch?v=ZCUXu4qEEfk)
• Information for Prospective and Newly Admitted Students
• University of Szeged
To successfully complete your application:
1. You need to send an e-mail (subject of the email : ESR8_E-MUSE_2nd Round) to:
Please include your CV (in English) and signed ESR Consent Form (ESR CF)
2. Click candidate on the button below and submit the Microsoft Form in English.
To fill the form use Chrome or Firefox browser.
Only candidates who follow the above instructions will be further considered for the recruitment process.