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As part of the COMET-PS project (https://www.nwo.nl/en/research-and-results/research-projects/i/17/27817.html), we have an opening for a 2 year (1 FTE) to 4 year (0.5 FTE) Postdoctoral position ...
As part of the COMET-PS project (https://www.nwo.nl/en/research-and-results/research-projects/i/17/27817.html), we have an opening for a 2 year (1 FTE) to 4 year (0.5 FTE) Postdoctoral position in the IEBIS group of University of Twente on the topic of ‘Complexity Methods for Predictive Synchromodality’.
Synchromodality is a highly powerful and promising concept for boosting the efficiency of freight transportation, based on combining multiple transportation modes (barges, trucks, trains) in a smart way. This makes a transition possible from the delivery of plain logistic services to integrated services by exploiting the complementary nature of available transportation modes.
In our approach, we first identify the particular business problems that require modelling. The aim behind the modelling is to create a purely data driven predictive model (without developing a mathematical model upfront) that provides reasonably accurate predictions in the complex domain and then to use machine learning approaches to find the optimum synchromodal assignment. The challenge being, that the more complex a domain is the harder it is to make good predictions, as more implicit domain knowledge is required that is not always available.
The goal of our approach is to design and develop predictive models, that can eventually be incorporated into a business intelligence dashboard and then use these predictions to find to optimize the synchromodal allocations. As a result, one would (i) understand the nature and origin of data that allows the system user to determine the quality of the data to perform the data cleaning; (ii) understand the factors in the domain that influence the predicted variable, leading the developer to determine which variables need to be included in the predictive model; (iii) develop predictive models that are usable and interesting within the domain in terms of predictive power, integrating with existing infrastructure, and integrating with business rules & processes; and finally (iv) use the predicted data to find the optimum synchromodal assignment.
The postdoctoral position is for three/four years, depending on whether the candidates chooses to work more full time (1 FTE) or part-time (0.5 FTE). The position can be extended subject to future grants. The postdoc would spend about half a year on improving and optimizing the collection of structured as well as unstructured data from the different stakeholders.
We are looking for self-motivated and ambitious individuals who have a PhD in computer science, information systems or a related discipline. The candidate should have knowledge of, and experience in, gathering, curating and analysing both structured and unstructured data using machine-learning algorithms (experience of statistical approaches are a plus). It is preferable that the candidate has a good working knowledge of Dutch (though not essential), as most of the data from the logistic providers are in Dutch. The candidate must have the desire to undertake and publish research that makes important theoretical and applied contributions to the field, and experience of publishing will be an advantage. Experience and affinity with research in logistics will also be an advantage. The candidate must be comfortable and willing to work with various academic, governmental and commercial agencies in the project.
Application Deadline: 15 Jan 2018. Candidates with a matching profile who have completed their PhD recently are encouraged to apply with a cover letter, CV and 3 of their best publications.
For more information please contact Chintan Amrit, email@example.com, Ph:0031534894064.
The University of Twente. We stand for life sciences and technology. High tech and human touch. Education and research that matter. New technology which leads change, innovation and progress in society. The University of Twente is the only campus university of the Netherlands; divided over five faculties we provide more than fifty educational programmes. We have a strong focus on personal development and talented researchers are given scope for carrying out groundbreaking research.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status or disability status. Because of our diversity values we do particularly support women to apply.
Faculty of Behavioural, Management and Social sciences
The Faculty of Behavioural, Management and Social sciences (BMS) strives to play a pivotal role in understanding, co-engineering and evaluating innovation in society. Innovation is driven by advances in technology. Through 'social engineering' these technological advances are embedded in society befitting human needs and behaviour, within proper public and private management and business structures. For this the faculty of BMS upholds high quality disciplinary knowledge in psychology, business administration, public administration, communication science, philosophy, educational science and health sciences. All with a focus on the challenges in society. Research is strongly connected to our Institutes on Governance (IGS), ICT (CTIT), Health (MIRA) and Nanotechnology (MESA+).