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ELLIS Delft Talk by Javier Alonso-Mora

ELLIS Delft Talk by Javier Alonso-Mora 12 April 2022 16:00 (NOTE: the meeting moved from 5th -> 12th) This will be a hybrid meeting This meeting is open for all interested researchers. Motion Planning among Decision-Making Agents: Trajectory Optimization with Learned Cost Functions Abstract We move towards an era of smart cities, where autonomous vehicles will provide on-demand transportation while making our streets safer and mobile robots will coexist with humans. The motion plan of mobile robots and autonomous vehicles must, therefore, account for the interaction with other agents and consider that they are, as well, decision-making entities. For example, when humans drive a car, they are fully aware of their environment and how other drivers and pedestrians may react to their future actions. Towards this objective I will discuss several methods for motion planning and multi-robot coordination that leverage constrained optimization and reinforcement learning to achieve interactive behaviors with safety guarantees. Namely: using inverse reinforcement learning and social value estimation to achieve social behaviors; employing a learned policy to guide the motion planner in dense traffic scenarios or for information gathering; achieving social trajectories by learning a cost function from a dataset of human-driven vehicles; and learning to communicate the relevant information for multi-robot coordination. The methods are of broad applicability, including autonomous vehicles and aerial vehicles. Bio Javier Alonso-Mora is an Associate Professor at the Department of Cognitive Robotics of the Delft University of Technology, the director of the Autonomous Multi-robots Laboratory, a Principal Investigator at the Amsterdam Institute for Advanced Metropolitan Solutions and co-founder of The Routing Company. Previously, he was a Postdoctoral Associate at the Computer Science and Artificial Intelligence Lab (CSAIL) of the Massachusetts Institute of Technology. He received his Ph.D. degree in robotics from ETH Zurich, in partnership with Disney Research Zurich. He serves as associate editor for Springer Autonomous Robots, and has served as associate editor for the IEEE Robotics and Automation Letters, the Publications Chair for the IEEE International Symposium on Multi-Robot and Multi-Agent Systems 2021 and associate editor for ICRA, IROS and ICUAS. He is the recipient of several prizes and grants, including an ERC Starting Grant (2021), the ICRA Best Paper Award on Multi-robot Systems (2019), an Amazon Research Award (2019) and a talent scheme VENI award from the Netherlands Organisation for Scientific Research (2017). More info: https://www.autonomousrobots.nl/ To join this event, please contact Frans Oliehoek .

van Duijvenbode, J.R.

Profile TU Delft (2018 – current) Ph.D. candidate in Resource Engineering I obtained a MSc degree in the European Mining Course (EMC) from Delft University of Technology, Aalto University and RWTH Aachen. My master thesis was about: Development and Validation of Short-term Mine Planning Optimization Algorithms for a Sublevel Stoping Operation with Backfilling. Research PhD research into the behavioural Geology – Understanding how differences in geology influence metallurgical performance. The research topic consists of integrating collected information on metallurgical properties, directly or through proxies back into the resource model. The consideration of metallurgical costs is the only way forward to obtain truly optimized mining decisions, accounting for constraints and bottlenecks in the comminution circuit and chemical processing plant. This is important to better characterize metallurgical behavior of the plant feed, which allows for a morel optimal selection of process control settings. The envisioned solution will result in an increased recovery in combination with a lower utilization of energy and chemicals per tonne of processed material (lower environmental footprint). Consequently, overall OPEX will drop making lower grade ore economic while increasing the mineral resources that are available for conversion to ore reserves (lesser need to open up new mines). Moreover, a better characterization of mining blocks reduces the unintended processing of waste due to lower overall classification errors. Copromotor: Dr. M. Soleymani Shishvan Promotor(s): Dr. M. Buxton and Prof. Jan Dirk Jansen Jeroen van Duijvenbode PhD Candidate + 31 15 27 82262 J.R.vanDuijvenbode@tudelft.nl Faculty of Civil Engineering and Geosciences Building 23 Stevinweg 1 / PO-box 5048 2628 CN Delft / 2600 GA Delft Room number: 3.21

ELLIS Delft Talk by Guillaume Rongier

ELLIS Delft Talk by Guillaume Rongier Going beyond empirical relationships in geology: The example of total organic carbon 01 February 2022 16:00 Abstract While machine learning has a long history in geology, empirical relationships remain widely used. Through the example of total organic carbon (TOC), this talk will illustrate the close links between empirical relationships and machine learning, and the benefits of turning to machine learning. TOC is a measure of the proportion of organic carbon in rock samples typically gathered from boreholes. It can be used to assess the potential for hydrocarbons, understand rock mechanics, or assess reducing conditions for basin-hosted mineral systems, and is paramount when seeking to understand variations in paleo-environmental conditions. Since gathering and analyzing rock samples is expensive, empirical relationships have been developed to predict TOC from well logs, which are based on more widely available geophysical measurements into boreholes. Those empirical relationships come from geological and petrophysical principles implemented in mathematical models manually fitted to the data. This leads to several limitations, mainly poor generalization, inability to quantify uncertainties, time-consuming and subjective calibration that leads to reproducibility issues. But those empirical relationships can be rewritten as linear regressions, a simple change that solves many of the previous limitations. Turning to more advanced machine learning methods improves predictions by taking into account the non-linearity and variability in the data. Using the expert knowledge behind empirical relationships as input besides well logs improves the predictions as well: this shows that leveraging geological and petrophysical concepts through feature selection and engineering boosts machine learning performances. To join this event, please contact Frans Oliehoek .

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ELLIS Delft Talk by Javier Alonso-Mora

ELLIS Delft Talk by Javier Alonso-Mora 12 April 2022 16:00 (NOTE: the meeting moved from 5th -> 12th) This will be a hybrid meeting This meeting is open for all interested researchers. Motion Planning among Decision-Making Agents: Trajectory Optimization with Learned Cost Functions Abstract We move towards an era of smart cities, where autonomous vehicles will provide on-demand transportation while making our streets safer and mobile robots will coexist with humans. The motion plan of mobile robots and autonomous vehicles must, therefore, account for the interaction with other agents and consider that they are, as well, decision-making entities. For example, when humans drive a car, they are fully aware of their environment and how other drivers and pedestrians may react to their future actions. Towards this objective I will discuss several methods for motion planning and multi-robot coordination that leverage constrained optimization and reinforcement learning to achieve interactive behaviors with safety guarantees. Namely: using inverse reinforcement learning and social value estimation to achieve social behaviors; employing a learned policy to guide the motion planner in dense traffic scenarios or for information gathering; achieving social trajectories by learning a cost function from a dataset of human-driven vehicles; and learning to communicate the relevant information for multi-robot coordination. The methods are of broad applicability, including autonomous vehicles and aerial vehicles. Bio Javier Alonso-Mora is an Associate Professor at the Department of Cognitive Robotics of the Delft University of Technology, the director of the Autonomous Multi-robots Laboratory, a Principal Investigator at the Amsterdam Institute for Advanced Metropolitan Solutions and co-founder of The Routing Company. Previously, he was a Postdoctoral Associate at the Computer Science and Artificial Intelligence Lab (CSAIL) of the Massachusetts Institute of Technology. He received his Ph.D. degree in robotics from ETH Zurich, in partnership with Disney Research Zurich. He serves as associate editor for Springer Autonomous Robots, and has served as associate editor for the IEEE Robotics and Automation Letters, the Publications Chair for the IEEE International Symposium on Multi-Robot and Multi-Agent Systems 2021 and associate editor for ICRA, IROS and ICUAS. He is the recipient of several prizes and grants, including an ERC Starting Grant (2021), the ICRA Best Paper Award on Multi-robot Systems (2019), an Amazon Research Award (2019) and a talent scheme VENI award from the Netherlands Organisation for Scientific Research (2017). More info: https://www.autonomousrobots.nl/ To join this event, please contact Frans Oliehoek .

van Duijvenbode, J.R.

Profile TU Delft (2018 – current) Ph.D. candidate in Resource Engineering I obtained a MSc degree in the European Mining Course (EMC) from Delft University of Technology, Aalto University and RWTH Aachen. My master thesis was about: Development and Validation of Short-term Mine Planning Optimization Algorithms for a Sublevel Stoping Operation with Backfilling. Research PhD research into the behavioural Geology – Understanding how differences in geology influence metallurgical performance. The research topic consists of integrating collected information on metallurgical properties, directly or through proxies back into the resource model. The consideration of metallurgical costs is the only way forward to obtain truly optimized mining decisions, accounting for constraints and bottlenecks in the comminution circuit and chemical processing plant. This is important to better characterize metallurgical behavior of the plant feed, which allows for a morel optimal selection of process control settings. The envisioned solution will result in an increased recovery in combination with a lower utilization of energy and chemicals per tonne of processed material (lower environmental footprint). Consequently, overall OPEX will drop making lower grade ore economic while increasing the mineral resources that are available for conversion to ore reserves (lesser need to open up new mines). Moreover, a better characterization of mining blocks reduces the unintended processing of waste due to lower overall classification errors. Copromotor: Dr. M. Soleymani Shishvan Promotor(s): Dr. M. Buxton and Prof. Jan Dirk Jansen Jeroen van Duijvenbode PhD Candidate + 31 15 27 82262 J.R.vanDuijvenbode@tudelft.nl Faculty of Civil Engineering and Geosciences Building 23 Stevinweg 1 / PO-box 5048 2628 CN Delft / 2600 GA Delft Room number: 3.21

ELLIS Delft Talk by Guillaume Rongier

ELLIS Delft Talk by Guillaume Rongier Going beyond empirical relationships in geology: The example of total organic carbon 01 February 2022 16:00 Abstract While machine learning has a long history in geology, empirical relationships remain widely used. Through the example of total organic carbon (TOC), this talk will illustrate the close links between empirical relationships and machine learning, and the benefits of turning to machine learning. TOC is a measure of the proportion of organic carbon in rock samples typically gathered from boreholes. It can be used to assess the potential for hydrocarbons, understand rock mechanics, or assess reducing conditions for basin-hosted mineral systems, and is paramount when seeking to understand variations in paleo-environmental conditions. Since gathering and analyzing rock samples is expensive, empirical relationships have been developed to predict TOC from well logs, which are based on more widely available geophysical measurements into boreholes. Those empirical relationships come from geological and petrophysical principles implemented in mathematical models manually fitted to the data. This leads to several limitations, mainly poor generalization, inability to quantify uncertainties, time-consuming and subjective calibration that leads to reproducibility issues. But those empirical relationships can be rewritten as linear regressions, a simple change that solves many of the previous limitations. Turning to more advanced machine learning methods improves predictions by taking into account the non-linearity and variability in the data. Using the expert knowledge behind empirical relationships as input besides well logs improves the predictions as well: this shows that leveraging geological and petrophysical concepts through feature selection and engineering boosts machine learning performances. To join this event, please contact Frans Oliehoek .
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Effect of vibrational modes on fluidization characteristics and solid distribution of cohesive micro- and nano-silica powders

Fluidization of powders with small particle sizes is typically troublesome due to their cohesive nature. These powders to not transition from a packed bed into a homogeneous fluidizing one upon the introduction of a gas flow. Rather, they tend to stay mostly stationary, forming vertical channels through which the gas can escape. Several methods have been studied to overcome this behaviour and initiate fluidization, one of which is vertical vibration. We hypothesized that a horizontal component of the vibration would be more effective in disrupting the channelling, since the vibration would work perpendicular to the channel direction. In our work we compared the fluidization quality of beds of micro- and nano-particles, subjected vertical and elliptical (a combination of vertical and horizontal) vibration. In contrast to our expectations, we found that adding a horizontal component mitigated the effect of the vibrations, to the point that channels mostly remained present in the bed, whereas solely vertically vibrated beds showed full fluidization. Additionally, utilizing sectional pressure drop measurements, we showed improvements in fluidization behaviour with respect to the superficial gas velocity, which could not be acquired through conventional indicators of fluidization. Finally, we confirmed our results by X-ray imaging, where the presence or absence of channels could easily be demonstrated. Rens Kamphorst, Kaiqiao Wu, Matthijs van Baarlen, Gabrie M.H. Meesters, J. Ruud van Ommen Rens Kamphorst Go to the publication

Hiring: Assistant Professor Water Resources Engineering

In collaboration with the Faculty of Civil Engineering and Geosciences, within the Flagship Water Security of our Climate Safety and Security Center (CASS) in the campus of the TU Delft in The Hague, we have a new opening for an Assistant Professor! Are you interested in driving innovation in water systems management and preparing the next generation of engineers for their climate and policy challenges? Apply now! Job description Key job responsibilities include: Education: Organize, initiate and contribute to the development and teaching of graduate courses in our new MSc program Environmental Engineering, fostering connections with other programs. Guide and assess BSc and MSc students, coordinating fieldwork, student projects, assignments and exams collaboratively. Organization: Contribute to organizational / administrative activities and committees focused on education within the Department of Water Management and the Faculty of Civil Engineering and Geosciences. Your involvement will be pivotal in advancing the Flagship Water Security of TU Delft | Climate Safety & Security Center (CASS). Impact: Drive the inception of educational initiatives and assets in launching new research projects with societal impact. Actively engage with government and private partners to increase societal relevance. Because of the joint affiliation with the TU Delft | Climate Safety & Security Center, seize opportunities to collaborate with the public sector, including Dutch Ministries and Policy Advisory Bodies, water management organizations as well as international public policy organizations. Outreach to the broader community and schools is encouraged. Research: Contribute to groundbreaking research in water resources engineering, closely connected to climate safety and security concerns. This role offers collaboration prospects with the Energy, Food, Materials and Human Security flagships within CASS, ensuring international visibility and impact. Requirements We invite you to show in your application how your expertise relates to the demands from the water domain, the safety and security domain and the education domain which this position aims to connect. Furthermore, you: hold a PhD or equivalent degree in water management, environmental science, civil engineering, environmental engineering, or a related discipline; demonstrate affinity with academic teaching across diverse settings, including empirical contexts; possess a solid understanding of the higher education landscape, including diversity and inclusion values; exhibit a track record in delivering high-quality research, as evidenced by your publication record; possess excellent communication skills, you are capable of effectively engaging with peers, students and stakeholders; demonstrate an affinity for, and preferably a proven ability to collaborate with, the public sector. Conditions of employment This position is offered as an Academic Career Track position (0.8 – 1.0 FTE). During the Academic Career Track, we expect you to grow towards an Associate Professor position within a maximum of eight years, for which a position will be available. With other Academic Career Track colleagues, you will participate in the Academic Career Track Development programme, where you are offered ample opportunities to develop yourself in the areas of Education, Research, Societal Impact & Innovation, and Leadership & Organisation. You will regularly discuss your development and results with senior staff based on a personalized development plan and performance criteria agreed upon at the start of your Academic Career Track. You will start with a temporary contract that will be converted to a permanent contract no later than 12 -18 months after a positive evaluation, based on continuous confidence in your development potential and fit in the organisation, Inspiring, excellent education is our central aim. We expect you to obtain a University Teaching Qualification (UTQ) within three years if you have less than five years of teaching experience. This is provided by the TU Delft UTQ programme as part of the Academic Career Track Development programme. TU Delft sets high standards for the English competency of the teaching staff. The TU Delft offers training to improve English competency. If you do not speak Dutch, we offer courses to learn the Dutch language within three years. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities. The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged and you can work partly from home. For international applicants, TU Delft has the Coming to Delft Service . This service addresses the needs of new international employees and those of their partners and families. The Coming to Delft Service offers personalised assistance during the preparation of the relocation, finding housing and schools for children (if applicable). In addition, a Dual Career Programme for partners is offered. The Coming to Delft Service will do their best to help you settle in the Netherlands. TU Delft (Delft University of Technology) Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context. At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration. Challenge. Change. Impact! Faculty Civil Engineering and Geosciences The Faculty of Civil Engineering & Geosciences (CEG) is committed to outstanding international research and education in the field of civil engineering, applied earth sciences, traffic and transport, water technology, and delta technology. Our research feeds into our educational programmes and covers societal challenges such as climate change, energy transition, resource availability, urbanisation and clean water. Our research projects are conducted in close cooperation with a wide range of research institutions. CEG is convinced of the importance of open science and supports its scientists in integrating open science in their research practice. The Faculty of CEG comprises 28 research groups in the following seven departments: Materials Mechanics Management & Design, Engineering Structures, Geoscience and Engineering, Geoscience and Remote Sensing, Transport & Planning, Hydraulic Engineering and Water Management. Click here to go to the website of the Faculty of Civil Engineering & Geosciences. Water Management The mission of the Department of Water Management is to advance fundamental scientific understanding of the water cycle, and to develop innovative engineering and water management solutions. Our main aim is to help solve key societal challenges related to water systems and their interactions with humans. These societal challenges include the impact of climate change and urbanization on water quantity and quality in natural and engineered systems, environmental and human health risk assessments, as well as the associated adaptation strategies, innovative water treatment technologies to produce clean water, and solutions for resource depletion on food security. Recently, an external research assessment committee rated the Department of Water Management `excellent’ on all three aspects reviewed: quality of research, viability and societal relevance. The department currently has 40+ FTE academic staff and over 100 PhD students and postdocs. This position is embedded in the newly founded interdisciplinary Climate Safety & Security Center (CASS) at TU Delft | The Hague. This center pursues an ambitious and extensive program that considers climate change and stability in an integrative way. It focuses on the flow of the essential commodities water, food, energy and critical materials. The new position plays a crucial role in the flagship Water. To help realize your ambitions, you will receive a generous start-up package including 1 PhD candidate within the scope of the CASS program. You would also collaborate with leading international researchers and have access to TU Delft’s state-of-the-art facilities. Additional information For more information about this vacancy, please contact Remko Uijlenhoet: r.uijlenhoet@tudelft.nl . Application procedure Are you interested in this vacancy? Please apply no later then 11 June 2024 via the application button and upload : A well-crafted motivation letter (1-2 pages) detailing your interest and suitability for the position. Your Curriculum Vitae (CV), highlighting relevant experiences, list of publications and achievements. Your statement on research and education including your view on leadership and commitment to equity and inclusion (maximum 3 pages). Contact information for four referees who could provide insightful recommendations. An abstract of your PhD thesis (1 page). Links to two selected publications that you wish to emphasize. Please note: You can apply online. We will not process applications sent by email and/or post. A pre-Employment screening can be part of the selection procedure. Please do not contact us for unsolicited services.

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