Please proceed at first to the Welcome Students pages here:
https://human-centered.ai/scientific-working-for-students

SP.67 OPEN DEV JOBS (any time)
Status
open
Worktype
OPEN DEV JOBS -- We are seeking devs for our Human-Centered AI projects --- OPEN DEV JOBS
Keywords
Frontent development, Java, Javascript/Webservices, App development, annotation bots, Docker, Angular.js, motion UI, intelligent interfaces, etc.
Goals
No medical knowledge necessary, no scientific interest required ... just programming, developing, coding, hacking - exactly what engineers love ...
For Students of
software engineering or related areas
Abstract
If you are a 1) student, 2) nerd in any of the modern program technologies and 3) would like to earn money with your hobby then you should send an e-Mail with a short CV listing your programming abilities directly to: andreas.holzinger AT medunigraz.at
Reading
You will work in an awesome and extremely exciting environment on cutting-edge AI/machine learning projects, together with international scientists and medical professionals in real-world scenarios on clear dev tasks, so you can completely concentrate on code hacking.
SP.66 PhD/Postdoc Positions OPEN in explainable AI (to be filled until December 2019)
Status
open
Keywords
Explainable AI (interpretable machine learning)
Goals
We are hiring! We are expanding our group in Machine Learning/Artificial Intelligence and we are are looking for research associates (PhD, Postdoc) interested in AI/ML with a special focus on weakly-supervised learning, explainable AI (interpretable machine learning) and transfer learning with an interest in the application field of digital pathology (gigapixel images). Tasks include: - Data pre-processing (Whole Slide Images) - Algorithm implementation - Experimental work - Collaboration within an international team - Collaboration with medical professionals Functional requirements: - Successfully completed computer science studies (at least a University Master's degree) - Excellent programming skills (e.g. Python) - Very good written and spoken English (ability to write scientific texts in English, presenting at international project meetings) Opportunities: - working in an exciting international environment on non-every-day-tasks on large amounts of real-world data in real-world settings - opportunity for stays at our cooperation partners - opportunity to be part of tutoring and teaching activities in machine learning/artificial intelligence Possible starting date: any time from now until December 2019 If this stimulates your interest please send a short motivation letter along with your CV to a.holzinger AT hci-kdd.org and do not forget to include the reference number from the students welcome page to tunnel through the mail filter.
SP.64 Building a Digital Pathology Deep Learning Machine (cool project)
Status
open
Worktype
All types of student work (Projekt Informationssysteme, Bachelor, Master, PhD - please look up [3])
Keywords
Deep Learning Machine, Digital Pathology
Goals
For our Digital Pathology project we will carry out some experiments with TensorFlow and compare this with fast a own built deep learning machine. More information in a personal conversation. Read the story by Lukas Biewald from February, 1, 2017 on building a super fast deep learning machine for under $1,000 [1] before making contact - this is a cool project for nerds 🙂 Read also [2].
For Students of
Students with interest in doing experimental work on a cool topic (AI + Medicine)
Required Knowledge
Programming experience
Additional Information

[1] https://www.oreilly.com/learning/build-a-super-fast-deep-learning-machine-for-under-1000

[2] https://hci-kdd.org/2017/10/26/digital-pathology-worlds-fastest-scanner-now-working-graz

[3] 706.505 Projekt Informationssysteme, 706.800, 706.801 Bachlorarbeit Informatik, 706.804, 706.805, Bachelorarbeit Softwarenetwicklung-Wirtschaft, 706.902 Bachelorarbeit Information and Computer Engineering, 706.501 Master-Projekt, 706.502 Master Project Web and Data Science, and 706.046 AK HCI https://online.tugraz.at/tug_online/wblvangebot.wbshowlvoffer?ppersonnr=5313

 

SP.61 Human-AI Interaction in Digital Pathology (to be filled between April and October 2019)
Status
open
Worktype
Bachelor, Master, Ph.D. interested in Human-AI Interaction and Microscopic Eyetracking in Pathology
Keywords
Digital Pathology
Goals
In the context of our large-scale Digital Pathology project MAKEpatho [1], [4] we are currently offering some theses in an exciting, challengenging and brand new environment [2]. We are working on cutting-edge methodologies with top-end equipment and have opportunities for a variety of different interesting work. There are several work streams available, e.g. in eye-tracking microscope, usability and sensor fusion for several studies to develop a mathematical model of observed cognitions of human experts versus novices, and for detecting patterns of cognition; the work is aimed at the comparison between human learning/decision making and machine learning/prediction and how both can benefit from each other in particular. If this raises your interest you should visit us. For inquiry please contact a.holzinger AT hci-kdd.org
For Students of
Students with interest in Human-Computer Interaction an affective computing for medical decision support
Reading
Please check with the students pages for general information [3]
Additional Information

[1] MAKEpatho  http://hci-kdd.org/project/makepatho

[2] News from 26.10.2017 http://hci-kdd.org/2017/10/26/digital-pathology-worlds-fastest-scanner-now-working-graz

[3] http://hci-kdd.org/scientific-working-for-students

[4] Andreas Holzinger, Bernd Malle, Peter Kieseberg, Peter M. Roth, Heimo Müller, Robert Reihs & Kurt Zatloukal 2017. Towards the Augmented Pathologist: Challenges of Explainable-AI in Digital Pathology. arXiv:1712.06657.

 

 

SP.59 ABC-TUGROVIS
Status
open
Worktype
ABC - Agent based Cancer Simulation
Keywords
machine learning, multi-agents
Abstract
Contrary to cellular automata approaches, discrete agent based models are very promising for simulating tumor growth. These can be extended to hybrid systems, also with the biologist-in-the-loop. In this master thesis (which can be the ground work for a PhD) it shall be experimented with state-of-the-art methods and contributed to a larger project on machine learning for tumour growth simulation and visualization to help to contribute towards two goals: supporting cancer resarch and help to reduce animal experiments.
Additional Information

Jeanquartier, F., Jean-Quartier, C., Cemernek, D. & Holzinger, A. 2016. In silico modeling for tumor growth visualization. BMC Systems Biology, 10, (1), 1-15, doi:10.1186/s12918-016-0318-8.

TUGROVIS Project Page

 

SP.58 Scenario-Based Transfer Learning Models
Status
open
Worktype
All levels, work will be adapted accordingly
Keywords
machine learning, transfer learning, agnostic learning
Required Knowledge
Python
Abstract
Humans are very good in transfer learning, i.e. we can solve new problems with regard to previously learned tasks, the classic example is to learn snowboarding from windsurfing, but maybe a better example is language learning: It is much easier to learn Spanish when knowing Italian. Automatic machine learning makes usually no use of such advantages and this is known as the problem of catastrophic forgetting. For two decades now this is an extremely hot topic in the machine learning community [1] and any advances may results in major breaktroughs in this area. In this work the student shall experiment with health related data and formalize some ideas on how to solve concrete transfer learning problems including a benchmarking and evaluation to existing transfer learning approaches.
Additional Information

[1] Thrun, S. 1996. Is learning the n-th thing any easier than learning the first? Advances in neural information processing systems (NIPS), 640-646.
online available

“The most rewarding research is the one that delights the thinker and at the same time is beneficial to humankind” (Christian Doppler, 1803‐1853). The Human-Centered AI Lab (Holzinger group) is devoted to the guiding principle: “Science is to test crazy ideas – Engineering is to put these ideas into Business” (A.Holzinger, 2011, Successful Management of R&D).

Holzinger-Group-Research-Approach