Post-doc
Offer DescriptionWe offer:
Job description:
The position involves participation in an interdisciplinary and pioneering project, “Intelligent Decision Support Based on Explanatory Analytics of Preference Data” led by Professor Roman Słowiński. The objective is to develop innovative algorithms and conduct computational experiments resulting in publications in leading scientific journals. Key focus areas include:
1. Consensus Reaching Process in Group Decision-Making with explanatory models of decision-makers’ preferences.
2. Enhanced Algorithms for Decision Rule Induction and Classifier Ensemble Construction composed of diverse sets of decision rules.Ad. 1. Based on the observation that many previous studies on group decision-making did not pay enough attention to individual participation and satisfaction of DMs in the decision-making process, we proposed in [Y. Zhao, Z. Gong, G. Wei, R. Słowiński, Consensus modeling with interactive utility and partial preorder of decision-makers, involving fairness and tolerant behavior. Information Sciences, 638 (2023) 118933, ] a new kind of consensus models for group utility optimization. An interesting follow-up of this study would consist in the application of robust ordinal regression for determining a representative collective utility function based on preference information reflecting the value systems of individual DMs. Another way of determining a representative collective preference model would be possible if individual preferences of DMs would be represented by “if…, then…” decision rules. It would be consistent with explainable preference analytics. Then, the preference information of all DMs provided in terms of pairwise comparisons or classifications of some reference alternatives would be used by Dominance-based Rough Set Approach to induce a collective rule preference model guiding interactively the consensus- reaching process.Ad. 2. All tasks of the project are based on representation of preferences in terms of „if…, then…” decision rule. The methodology of rule induction from ordinal data employs Dominance-based Rough Set Approach. The methodology has been described in [M. Szeląg, R. Słowiński, Explaining and predicting customer churn by monotonic rules induced from ordinal data. European Journal of Operational Research, 317 (2024) no.2, 414-424. Another topic of decision rule induction that is worth investigation is construction of ensemble classifiers composed of diversified basic classifiers. The basic classifiers would be sets of “if…, then…” decision rules obtained by algorithms developed within this task. The method for finding diversified basic classifiers would rely on preliminary results obtained in [J. Błaszczyński, B. Prusak, R. Słowiński: Multi-objective search for comprehensible rule ensembles. In: V. Flores et al. (eds.): IJCRS 2016, LNAI 9920, Springer, Berlin, 2016, pp. 503-513, ].Where to apply E-mailroman.slowinski@put.poznan.plRequirementsResearch Field Computer science » Other Education Level PhD or equivalentResearch Field Economics » Econometrics Education Level PhD or equivalentResearch Field Economics » Management studies Education Level PhD or equivalentResearch Field Mathematics » Other Education Level PhD or equivalentSkills/QualificationsRequirements:
1. A Ph.D. degree obtained1 not earlier than January 1st, 2018, in one of the following scientific disciplines: computer science, econometrics, management, or mathematics.(This period may be extended by the time spent during this period on long-term (over 90 days) documented sick leave or rehabilitation benefits due to incapacity for work. Additionally, this period can be extended by the number of months spent on leave related to childcare and upbringing granted according to the principles set out in the Labor Code. In the case of women intending to participate in the competition, it can be extended by 18 months for each born or adopted child, if this method of indicating career breaks is more beneficial.)
2. The Ph.D. degree should have been obtained from an institution other than the Poznan University of Technology, or the candidate has completed at least a 10-month continuous and documented postdoctoral fellowship at an institution other than the Poznan University of Technology and in a country other than the country of obtaining the Ph.D. degree.
3. Basic knowledge of operational research and artificial intelligence, particularly in intelligent decision support systems.
4. Familiarity with basic programming languages (Python, Java).
5. Documented research experience with publications in the required knowledge area.
6. At least a good command of English in both speech and writing.
7. Attributes such as availability, willingness for self-development, strong motivation for research work, creativity in problem-solving, independence, reliability, and teamwork skills.
Poznań, wielkopolskie
Fri, 01 Nov 2024 02:16:01 GMT
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