1,153 zoekresultaten voor “machine biodiversity” in de Publieke website
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Urban green infrastructure for biodiversity and ecosystem services
Urbanisation is steadily increasing with estimates expecting that 68% of the global population will live in cities by 2050. To accommodate the rural to urban transition, large quantities of land are transformed from natural to urban lands.
- Biodiversity and the Anthropocene
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Biodiversity and Sustainability (MSc)
In deze opleiding streef je naar het beantwoorden van biologische vraagstukken in relatie tot milieu, ecologie en andere maatschappelijk relevante onderwerpen.
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Massively collaborative machine learning
Promotor: J. N. Kok, Co-promotor: A. J. Knobbe
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Machine Learning
Computers zijn in staat om ongelooflijk nauwkeurige voorspellingen te doen op basis van machine learning. Met andere woorden, deze computers kunnen zonder tussenkomst leren als ze eenmaal door mensen zijn voorgeprogrammeerd. Bij LIACS verkennen en verleggen we de grenzen van wat een revolutionaire nieuwe…
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Efficient tuning of automated machine learning pipelines
Automated Machine Learning (AutoML) is widely used to automatically build a suitable practical Machine Learning (ML) model for an arbitrary real-world problem, reducing the effort of practitioners in the ML development cycle for real-world applications. Optimization is a key part of a typical AutoML…
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global regionalized impacts of eutrophication on freshwater fish biodiversity
Freshwater biodiversity has been threatened by eutrophication due to excessive nutrients in the environment. Releasing the freshwater species from such pressures requires efforts from industry and manufacturers to avoid emissions to vulnerable and high-risk regions.
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Quantum Methods for Machine Learning and Classical Dynamics
All the data stored and processed by our computers is encoded as sequences of zeros and ones, called bits. Quantum computers offer an alternative to this traditional way of encoding and manipulating information.
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Hybrid Quantum-Classical Metaheuristics for Automated Machine Learning Applications
This thesis investigates how quantum, quantum-inspired, and hybrid quantum-classical computation can enhance key points of the automated machine learning (AutoML) pipeline under the constraints of noisy intermediate-scale quantum (NISQ) devices.
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Reliable and Fair Machine Learning for Risk Assessment
The focus of this thesis is on the technical methods which help promote the movement towards Trustworthy AI, specifically within the Inspectorate of the Netherlands.
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Remote (sensing) functional biodiversity: exploring drivers of trait variation and spectral variability in the Arctic
Globally and regionally, biodiversity is declining and there are shifts in species’ occurrences and functional traits.
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Computational speedups and learning separations in quantum machine learning
This thesis investigates the contribution of quantum computers to machine learning, a field called Quantum Machine Learning. Quantum Machine Learning promises innovative perspectives and methods for solving complex problems in machine learning, leveraging the unique capabilities of quantum computers…
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Kunstmatige intelligentie en machine learning
Computers zijn in staat om ongelooflijk nauwkeurige voorspellingen te doen op basis van machine learning. Met andere woorden, deze computers kunnen zonder tussenkomst leren als ze eenmaal door mensen zijn voorgeprogrammeerd. Bij LIACS verkennen en verleggen we de grenzen van wat een revolutionaire nieuwe…
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Machine learning-based NO2 estimation from seagoing ships using TROPOMI/S5P satellite data
The marine shipping industry is one of the strongest emitters of nitrogen oxides (NOx), a pollutant detrimental to ecology and human health. Over the last 20 years, the pollution produced by power plants, the industry sector, and cars has been decreasing.
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Machine learning and computer vision for urban drainage inspections
Sewer pipes are an essential infrastructure in modern society and their proper operation is important for public health. To keep sewer pipes operational as much as possible, periodical inspections for defects are performed.
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Automated machine learning for dynamic energy management using time-series data
Time-series forecasting through modelling sequences of temporally dependent observations has many industrial and scientific applications. While machine learning models have been widely used to create time-series forecasting models, creating efficient and performant time-series forecasting models is…
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Automated Machine Learning for Neural Network Verification
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Digging for data: the rise and fall of a Miocene mammal biodiversity hotspot in the Vallès-Penedès (Catalonia, Spain)
The Vallesian, 11.1-9 Ma, was a special time in the Vallès-Penedes basin near Barcelona, where a biodiversity hotspot existed. Europe had a subtropical climate, with rhinos, forest giraffes, lions, hyenas, flying squirrels and primates.
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space of post-translationally modified peptides in Streptomyces with machine learning
The ongoing increase in antimicrobial resistance combined with the low discovery of novel antibiotics is a serious threat to our health care.
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Machine learning for radio galaxy morphology analysis
We explored how to morphologically classify well-resolved jetted radio-loud active galactic nuclei (RLAGN) in the LOw Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) using machine learning.
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Towards an effective biodiversity conservation and governance in the Pontocaspian region
Freshwater and brackish water ecosystems are arguably the most vulnerable ecosystems on earth, due to concentrated human developments in and around them. The Pontocaspian (PC) region located at the border of Europe and Asia contains a variety of brackish water ecosystems and unique inhabitants, known…
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The holographic glass bead game: from superconductivity to time machines
Promotores: Prof.dr. J. Zaanen, Prof.dr. K.E. Schalm
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Kunstgebitten, machines en stof
Over onorthodoxe uitingen van wetenschap.
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Data-Driven Machine Learning and Optimization Pipelines for Real- World Applications
Machine Learning is becoming a more and more substantial technology for industry.
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Information-theoretic partition-based models for interpretable machine learning
In this dissertation, we study partition-based models that can be used both for interpretable predictive modeling and for understanding data via interpretable patterns.
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Mens in de machine (najaar 2022)
Eerlijke en creatieve AI
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Solving the Gravitational N-body Problem with Machine Learning
In this work, I explore the creation of new methods that optimize simulations of the gravitational N-body problem. Specifically, I take advantage of the recent popularity of Machine Learning methods to find tools that can suit this problem.
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Unsprayed field margins: effects on environment, biodiversity and agricultural practice
A management strategy has been developed for field margins to reduce pesticide drift to non-target areas and to promote biodiversity on arable land. To this end, 3 and 6 m wide strips along the edges of winter wheat, sugar beet and potato crops have been left unsprayed with herbicides and insecticides…
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The flux and flow of data: connecting large datasets with machine learning in a drug discovery envirionment
This thesis focuses on data found in the field of computational drug discovery. New insight can be obtained by applying machine learning in various ways and in a variety of domains. Two studies delved into the application of proteochemometrics (PCM), a machine learning technique that can be used to…
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Interactions of Human Mobility and Farming Systems and Impacts on Biodiversity and Soil Quality in the Western Highlands of Cameroon
Promotors: Prof.dr. G.R. de Snoo, Prof.dr. G.A. Persoon, Prof.dr.ir. H.H. de Iongh
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The resilience of tropical intertidal seagrass meadows, grazed by dugongs, and the impact of anthropogenic stressors
Seagrass is a marine flowering plant with special adaptations to coastal and marine environments, playing a crucial role in providing a wide range of ecosystem services.
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Pharmacokinetics Nonlinear BBB Transport, Inter-species Scaling, and Machine Learning
This thesis focuses on enhancing predictions of central nervous system drug exposure using the LeiCNS-PK3.0, a physiologically based pharmacokinetic model.
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Species Literacy: The perception and cultural portrayal of animals
In his dissertation Michiel Hooykaas outlines the results of six empirical research projects focused at biodiversity awareness in the Netherlands, specifically people’s knowledge about animals.
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Dit platform maakt machine learning transparanter en toegankelijker
Wat ooit begon als een PhD-project, is uitgegroeid tot een website met jaarlijks 120.000 unieke bezoekers. Met het platform OpenML wil onderzoeker Jan van Rijn bijdragen aan open science, en zo machine learning transparanter, toegankelijker en eerlijker maken.
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Machine learning voorspelt voorkeuren
Cláudio de Sá voorspelt voorkeuren van mensen door gebruik te maken van ranglijsten. Dit doet hij door ‘klassieke’ machine learning-technieken aan te passen. Zijn werk kan onder andere gebruikt worden om de uitslagen van verkiezingen te voorspellen. Promotie op 16 december.
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The Little Green Machine
Onderzoekers uit de informatica, wiskunde, meteorologie, materiaalbouw natuur- en sterrenkunde hebben gezamenlijk een oplossing gevonden voor hun grote behoefte aan rekenvermogen. De diverse groep onderzoekers heeft voor de bouw van een revolutionaire supercomputer, die
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Microbial communities in Pampa soils; impact of land use changes, soil type and climatic conditions
Promotor: J.A. van Veen, Co-promotor: E.E. Kuramae
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Hearing what singing fish tell us about healthy oceans
Ontwikkelingen om de mariene biodiversiteit met hoge resolutie te meten, door ernaar te luisteren.
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Quantum machine learning: on the design, trainability and noise-robustness of near-term algorithms
This thesis addresses questions on effectively using variational quantum circuits for machine learning tasks.
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Theory of mind in language, minds, and machines: a multidisciplinary approach
Humans can see the world through the eyes of other humans and imagine what they know, want, and intend. This competence is known as Theory of Mind.
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How can humans and machines collaborate in a meaningful way in a restrictive environment?
In this project, researchers from computer science, law, psychology, and public administration research in practice how artificial intelligence (AI) can be leveraged to make decision-making in the security domain more effective, while also keeping it safe and accountable.
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Separating quantum and classical computing: rigorous proof and practical application
This thesis probes under what conditions quantum computing presents an advantage over classical computing.
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Matter into context: population- and community-level impacts of nanomaterials in freshwater ecosystems
The application of nanomaterials in industrial processes and consumer products provides many societal benefits, but can also lead to the release of nanomaterials into the environment. The work in this dissertation aims to provide insights into the potential environmental impacts that may follow from…
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Zelflerende machines voor beter begrip van heelal
Felle explosies van licht en zwaartekrachtgolven gaan overal over de hemel af. Zelflerende machines kunnen deze pas ontdekte dynamische kant van het heelal in kaart brengen. Hiervoor kent de Nationale Wetenschapsagenda 5 miljoen euro toe aan CORTEX, het Centrum voor Onderzoek in Real Time naar het Explosieve…
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Frederic Lens benoemd tot hoogleraar Biodiversity and Anatomy of Plants
Het IBL heeft een nieuwe hoogleraar: Frederic Lens is per 1 maart hoogleraar Biodiversity and Anatomy of Plants. De benoeming voelt voor Lens als een prachtige erkenning. ‘Ik ben blij met de waardering van de Universiteit Leiden voor mijn inzet op het gebied van onderzoek en onderwijs, en mijn rol als…
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PNAS Paperprijs voor quantum machine learning
‘We hopen dat ons artikel de mogelijkheden en voordelen laat zien van het gebruik van kunstmatige intelligentie in de quantumfysica om nieuwe ontdekkingen te doen.’ Vedran Dunjko van het Leiden Institute of Advanced Computer Science droeg bij aan een artikel dat vorig jaar verscheen in PNAS. Het artikel…
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Plant occurrence in space and time: the importance of land use, habitat structure, and pollination mode
Plant diversity is essential for us and our planet as it sustains the stability of our ecosystems, provides vital materials and food to us and supports many ecosystem services.
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Modelling the interactions of advanced micro- and nanoparticles with novel entities
Novel entities may pose risks to humans and the environment. The small particle size and relatively large surface area of micro- and nanoparticles (MNPs) make them capable of adsorbing other novel entities, leading to the formation of aggregated contamination.
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Optimally weighted ensembles of surrogate models for sequential parameter optimization
It is a common technique in global optimization with expensive black-box functions to learn a surrogate-model of the response function from past evaluations and use it to decide on the location of future evaluations.
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Exploring big data approaches in the context of early stage clinical
Als gevolg van de grote technologische vooruitgang in de gezondheidszorg worden in toenemende mate gegevens verzameld tijdens de uitvoering van klinische onderzoeken.