World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
49
Citations
10490
World Ranking
4268
National Ranking
87

Paj Peter Hilbers publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Paj Peter Hilbers sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 227 publications — 57th percentile

57% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Paj Peter Hilbers D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Paj Peter Hilbers sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 49 D-Index — 57th percentile

57% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Paj Peter Hilbers is affiliated with Eindhoven University of Technology in the Netherlands. Their research spans multiple fields primarily within Biochemistry, Genetics and Molecular Biology, as well as Medicine.

Their main subfields of study include Molecular Biology, Physiology, Oncology, Immunology, and Biomedical Engineering. These subfields reflect a focus on cellular processes, disease mechanisms, and the interplay between biological systems and engineering approaches.

Hilbers' research covers topics such as Microbial Metabolic Engineering and Bioproduction, Cancer Immunotherapy and Biomarkers, Single-cell and Spatial Transcriptomics, Immune Cells in Cancer, Diet and Metabolism Studies, Metabolomics and Mass Spectrometry Studies, and Adipose Tissue and Metabolism.

Significant recent publications by Hilbers include the following:

  • Interpretable systems biomarkers predict response to immune-checkpoint inhibitors, 2021, Patterns
  • A Distance-Based Framework for the Characterization of Metabolic Heterogeneity in Large Sets of Genome-Scale Metabolic Models, 2020, Patterns
  • Metabolic Modeling Combined With Machine Learning Integrates Longitudinal Data and Identifies the Origin of LXR-Induced Hepatic Steatosis, 2021, Frontiers in Bioengineering and Biotechnology
  • Simulating Metabolic Flexibility in Low Energy Expenditure Conditions Using Genome-Scale Metabolic Models, 2021, MDPI (MDPI AG)
  • Predictive systems biomarkers of response to immune checkpoint inhibitors, 2021, bioRxiv (Cold Spring Harbor Laboratory)

Hilbers has collaborated frequently with several researchers, including:

  • N.A.W. van Riel
  • Andrea Cabbia
  • Óscar Lapuente-Santana
  • Maisa N.G. van Genderen
  • Francesca Finotello

Their publications have appeared repeatedly in certain venues, indicating established contributions to these scholarly communities. Frequent publication venues include:

  • Patterns
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Frontiers in Bioengineering and Biotechnology
  • MDPI (MDPI AG)
  • Research Square (Research Square)

Best Publications

  • The role of collagen in bone apatite formation in the presence of hydroxyapatite nucleation inhibitors

    F Fabio Nudelman;K Koen Pieterse;A George;Phh Paul Bomans

  • Pathway complexity in supramolecular polymerization

    Peter A. Korevaar;Subi J. George;Albert J. Markvoort;Maarten M. J. Smulders

  • Simulating the self-assembly of gemini (dimeric) surfactants.

    S Karaborni;K Esselink;Paj Peter Hilbers;B Smit

  • Computer simulations of a water/oil interface in the presence of micelles

    B. Smit;P. A. J. Hilbers;K. Esselink;L. A. M. Rupert

  • A theoretical framework for strain-related trabecular bone maintenance and adaptation

    R. Ruimerman;P.A.J. Hilbers;B. van Rietbergen;R. Huiskes

  • Efficient Monte Carlo methods for the simulation of catalytic surface reactions

    JJ Johan Lukkien;Jpl John Segers;Paj Peter Hilbers;RJ Gelten

  • The Twisted Cube

    Peter A. J. Hilbers;Marion R. J. Koopman;Jan L. A. van de Snepscheut

  • Structure of a water/oil interface in the presence of micelles: A computer simulation study

    B. Smit;P.A.J. Hilbers;K. Esselink;L.A.M. Rupert

  • An equilibrium model for chiral amplification in supramolecular polymers.

    Huub M. M. ten Eikelder;Albert J. Markvoort;Tom F. A. de Greef;Peter A. J. Hilbers

  • Computer simulations of surfactant self assembly.

    B. Smit;P. A. J. Hilbers;K. Esselink

  • Computer simulations of surfactant self-assembly.

    B. Smit;K. Esselink;P.A.J. Hilbers;N.M. van Os

  • Monte Carlo simulations of a simple model for the electrocatalytic CO oxidation on platinum

    Mtm Marc Koper;Apj Tonek Jansen;van Ra Rutger Santen;JJ Johan Lukkien

  • Theoretical models of nonlinear effects in two-component cooperative supramolecular copolymerizations

    Albert J. Markvoort;Huub M.M. ten Eikelder;Peter A.J. Hilbers;Tom F.A. de Greef

  • Programmable chemical reaction networks: emulating regulatory functions in living cells using a bottom-up approach

    Hendrik W. H. van Roekel;Bas J. H. M. Rosier;Lenny H. H. Meijer;Peter A. J. Hilbers

  • Molecular dynamics simulations of oil solubilization in surfactant solutions

    S. Karaborni;N. M. van Os;K. Esselink;P. A. J. Hilbers

  • Parameter uncertainty in biochemical models described by ordinary differential equations.

    J. Vanlier;J. Vanlier;C.A. Tiemann;C.A. Tiemann;P.A.J. Hilbers;P.A.J. Hilbers;N.A.W. van Riel;N.A.W. van Riel

  • A Bayesian approach to targeted experiment design

    J. Vanlier;C. A. Tiemann;P. A. J. Hilbers;N. A. W. van Riel

  • Molecular dynamics study of nucleation and melting of n‐alkanes

    K. Esselink;P. A. J. Hilbers;B. W. H. van Beest

  • Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction

    Mark Heeswijk;Yoan Miche;Tiina Lindh-Knuutila;Peter A. Hilbers

  • Interpretable systems biomarkers predict response to immune-checkpoint inhibitors.

    Óscar Lapuente-Santana;Maisa van Genderen;Peter A.J. Hilbers;Francesca Finotello

  • Molecular dynamics study of the influence of wall-gas interactions on heat flow in nanochannels.

    Albert. J. Markvoort;P.A.J. Hilbers;S.V. Nedea

Frequent Co-Authors

Berend Smit
Berend Smit École Polytechnique Fédérale de Lausanne
Rik Huiskes
Rik Huiskes Eindhoven University of Technology
Rutger A. van Santen
Rutger A. van Santen Eindhoven University of Technology
Albert K. Groen
Albert K. Groen University of Amsterdam
E. W. Meijer
E. W. Meijer Eindhoven University of Technology
Emiel J. M. Hensen
Emiel J. M. Hensen Eindhoven University of Technology
Peter Stephan
Peter Stephan Technical University of Darmstadt
J.W. Niemantsverdriet
J.W. Niemantsverdriet Syngaschem (Netherlands)
Guofu Zhou
Guofu Zhou South China Normal University
Nico A. J. M. Sommerdijk
Nico A. J. M. Sommerdijk Radboud University Medical Center

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