|Publicatiedatum||13 juni 2019|
|Sluitingsdatum||30 juli 2019|
|Functieomvang||38 hours per week|
|Salarisindicatie||€2,325 to €2,972 gross per month|
The Amsterdam Business School (ABS) is part of the Faculty of Economics and Business of the University of Amsterdam (UvA) . We have an international population of over 3,000 students and are located in the centre of Amsterdam, the cultural and financial capital of the Netherlands. The facilities of the campus are modern and Amsterdam is an excellent place to live where people are friendly, tolerant and the ease with other cultures and languages. ABS has a broad portfolio of outstanding teaching and research programmes and is characterised by its international focus, the strong ties to the city of Amsterdam and its focus on several key areas such as finance, entrepreneurship, business analytics/big data and business & society/CSR.
The Department of Operations management of the Amsterdam Business School is involved in research in a broad range of areas of operations management, industrial statistic, operations research, information management, big data, data science, computer science and management science.
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 this data driven research, 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. This is especially true in a complex supply chain domain where there are soft factors like the interaction of the conflicting and cooperating objectives of the stakeholders and system dynamics of the uncertainty of the transport network play a significant role.
This research would be embedded within the NWO project titled COMET-PS and would be jointly supervised by researchers in UvA and VU. The goal of this research is to design and develop predictive and prescriptive models, that could eventually be incorporated into a business intelligence dashboard and then use these predictions 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 optimize synchromodal assignment.
We expect the candidate to have:
For further information you may contact:
You will be appointed for an initial period of 18 months with a possibility to extend it for another 2,5 years, pending positive evaluation. As part of your contract, you will spend 20% of your time on teaching. You will be classified as PhD candidate (promovendus) in the Dutch University job-ranking system (UFO), providing a gross monthly starting salary of €2,325 to €2,972 gross per month with an additional end-of-year bonus (8.3%) and holiday allowance (8%), in accordance with the Collective Labour Agreement of Dutch Universities.
The UvA is an equal-opportunity employer. We prioritise diversity and are committed to creating an inclusive environment for everyone. We value a spirit of enquiry and perseverance, provide the space to keep asking questions, and promote a culture of curiosity and creativity.
Applicants should send their CV and a covering letter and apply via the link below. Please include job reference number 19-390 and send your application before 31 July 2019. #LI-DNP
No agencies please